Technical Report 411 11'

DATA ANALYSIS METHODOLOGY FOR

DAY/NIGHT INFLIGHT TACTICAL NAVIGATION

to Edward M. Connelly and Robert F. Comeau

Performance Measurement Associates, Inc.

DTIC

,€ ARI FIELD UNIT AT FORT RUCKER, ALABAMA S ELECTEfl APR 4 T98

I

U.S. Army

Research Institute for the Behavioral and Social Sciences

mp September 1979

Approved fo li~i~ales distr 1 "-°'' c"" °'''4b lion 

''unlimited. '''' 043 .
U. S. ARMY RESEARCH INSTITUTE

FOR THE BEHAVIORAL AND SOCIAL SCIENCES

A Field Operating Agency under the Jurisdiction of the

Deputy Chief of Staff for Personnel

WILLIAM L. HAUS1%

JOSEPH ZEIDNER Colonel, U S Army Technical Director Commander

Research accomplished for the

Department of the Army

Performance Measurement Associates, Inc.

NOTICES

DISTRIBUTION: Primary distribution of this report has been made by ARI. Please address cofrespondenze concerning distribution of reports to: U. S. Army Research Institute for the Behavioral and Social Sciences.

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thes U. S. Army Research I nstitute for the Seheioral andO Social Scienals.

PAM. The findings in this report are not to be Construed as an official Departmetnt of the Armny position. uless so designated by other euthormeled documeants.
Unclassified

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. REP REOR ORTDOMENTATION DOUETTINPG D E0 BEFORE RO COMPLETING METRUCTIOSFORM

I. OEPORT NUMEER i. GOVT ACCESSION NO 3. RECIPEYTS CA.T .UMER

Technical Report 411

:INFLrGHT 5ATAENALYSISrETHODOLOGY TACTI CAL NAVIGATION,- FOri DAY/NIGHT -_ I %.| 19 MarI 79OL I NM

I ISR STT GTNN NT NUMBER

'AU';2 -M: .. 7 GRNT UM.?.

itEdward tMecnnelly by1persnnelCofthesAR FedUi 'Frc r

T oerai flighte

;. IILF~l"OMIG ORGANIZATION NAME AND ADDRESS 10. PROGRAMd ELEMENT. PROJECT. TASK

Performance Measurement Associates, Inc. A WORK U M BE RS

131 Park Street, N.E. Q263743A772

Vienna, VA 22180 ...

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Director of Training Developments bepogn n t

U.S. Army Aviation Center r or rm

Fort Rucker, AL 36362 oc 118 tp 1. MONITORING AGENCY NAME & AORESS(idirent hrom Confrollng Offce) 15, SECURITY CLASS. (of report) fi 

U.S. Army Research Institute for the Behavioral Unclassified and Social Sciences1

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It. KEY WORDS (Conltinue anl roe~rel side it necessaryi3 end hIdentify by block niumber)

Terrain flight

Navigation

NOE

Performance measurement

20r" il fine otvtw .A-- k -- naem and. Identify by blocknmbe)

17Lwlvlhelicopter navigation, both at night and during the day, is an essential portion of the Army's mission. Navigation performance, defined as

the probability of navigating along a prescribed route or route segment without deviation, is a function of many factors including map type, terrain type,

and time of day. Determining the effects of these factors is important for

predicting performance of Army navigators, identifying factors critical to

navigation, evaluating the probability of navigation success along a route, _

( Continued)

I 1 ) F A O N "0 1 Wl 3 c o on o n o r I o v 6 5 i s O s s o L E T E U c a s f e

SECURITY CLASSITOftIS t ;;e*
Unclassified

SECURITY CLASSIFICATION OF THIS PAGIT(WTIa Data taem")

Item 20 (Continued)

and developing training aids. The research reported here analyzed low-level

flight experiment data to develop that information.

This report contains results of the analyses of helicopter low-level

navigation data, (a) to determine the probability of navigation success

along a route as a function of terrain type, (b) to develop a means of

determining route difficulty, and (c) to develop a method of scoring student

performance. The FORTRAN computer programs and the navigation data base

used in the analysis are also documented.,,

SL~SiI o .I)

WTIZ W.£ Ii

IDC TAB

Juptn:, ,, , tt

" Avil w .,l i or"...

II Unclassified

SE[CURIITY CLASSIICATION OF THIS PAGE~ften Dael Entered)
Technical Report 411

DATA ANALYSIS METHODOLOGY FOR

DAY/NIGHT INFLIGHT TACTICAL NAVIGATION

Edward M. Connelly and Robert F. Comeau

Performance Measurement Associates, Inc.

James A. Bynum, Technical Team Manager

Submitted by: Charles A. Gainer, Chief

ARI FIELD UNIT AT FORT RUCKER, ALABAMA

Approved by:

Milton S, Katz. Acting Director

ORGANIZATION AND SYSTEMS

RESEARCH LABORATORY

U.S. ARMY RESEARCH INSTITUTE FOR THE BEHAVIORAL AND SOCIAL SCIENCES

5001 Eisenhower Avenue, Alexandria. Virginia 22333

Office, Deputy Chief of Staff for Personnel

Department of the Army

September 1979

Army Project Number Flight Training and

20263743A772 Aviator Selection

Approved for public release; distribution unlimited.

iii
ARI Research Reports and Technical Reports are intended for sponsors of

R&D tasks and for other research and military agencies. Any findings ready

for implementation at the time of publication are presented in the last part

of the Brief. Upon completion of a major phase of the task, formal recommendations for official action normally are conveyed to appropriate military

agencies by briefing or Disposition Form.

iv
FOREWORD

The Army Research Institute Field Unit at Fort Rucker, Alabama, is

involved in a variety of research and development activities as it seeks

to fulfill its mission of aircrew performance enhancement in the tactical environment. These activities include work in aircrew selection and

training and are responsive to Army ROTE Project 2Q263743A772 and the

Director of Training Developments, US Army Aviation Center. The work is

conducted in-house and is augmented by contracts with organizations havIng unique capabilities in specific areas.

This report is of a contract effort which concerns the complex

problem of establishing a suitable method of measuring day and night

navigation performance when conducting flight at terrain flight altitudes. Its objectives were to develop a method of evaluation which

would accurately describe navigation performance and which would be

universally applicable in the institutional aviator training program

and the ongoing field training program for the Army aviator.

0

(JSEPH Z NER

chnical Director

V
DATA ANALYSIS METHODOLOGY FOR DAY/NIGHT INFLIGHT

TACTICAL NAVIGATION

BRIEF

Requtrement:

Knowledge of the effect of terrain, vegetation, hydrograph9

and man-made features on the probability of helicopter navigation

success is necessary to train student navigators to select flight

routes and cues along a route. The objective of this study was

to develop and validate methods of analyzing flight data, and to

develop a slide rule for computing subject scores.

Procedure:

Flight data which consisted of maps marked with prespecified

and actual flight routes were analyzed to identify the available

navigation cues. For this analysis, the routes were divided into

intervals so that the available cues (terrain, vegetation, hydrographic and man-made features) along the route could be assigned

to intervals. Each interval was also categorized according to the

number of navigation success and failures that occurred in that

interval. From these data, the probability of navigation success

in an interval as a function of the available cues was determined.

Navigation success is defined as navigating along a prespecified

route with deviations less than 100 meters. The data was also

used to develop a slide rule suitable for scoring student navigation

flights.

Findings:

Of the 45 terrain types of terrain features tested, one

feature - the proportion of the route in one valley - was found

to be the most significant single feature for predicting route

probability of success.

vii

/
Terrain cues for navigation should be selected depending

on the "proportion of route in one valley" factor. Different

terrain features aid or hinder navigation performance depending

on that factor.

Navigation performance, as measured by the probability

of navigation success (PS), is a monotonic function of the number

of terrain features of a given type - except where multiple draws

preceed a draw where a route turn is required.

The route probability of success PS can be calculated by

the product of the constituent interval PS's.

According to the evidence developed, the ambient light

level does not affect navigation performance.

Evidence does not support the assertion that the existence )

of one or more clearings affects navigation performance; but,

does support the assertion that clearings serve as dividers

separating possible route paths.

V

I

viii
CONTENTS

Page

INTRODUCTION 1

OBJECTIVES 1

SUMMARY 2

GENERAL METHOD 3

NIGHT NAVIGATION ANALYSIS 7

Effect of Local Terrain Features on Interval PS 7

Analysis A: Frequency of the Joint Occurrence 7

of Features

Method 7

Results 8

Analysis B: Local Terrain Features Considered 8

Individually

Method 8

Results 12

Analysis C: Combinations of Terrain Feature Types 12

Method 12

Results 12

Analysis D: Existence of Clearings in an Interval 16

Method 16

Results 16

Analysis E: Multiple Clearing Patterns Along a Route 16

Method 16

Results 16

Effect of General Terrain Features on Route Segment PS 17

Analysis F: General Terrain Features 17

Method 17

Results 18

ix
CONTENTS

Page

Analysis G: Evaluation of the Length 21

Restriction on "l Valley"

Method 21

Results 23

Analysis H: "1 Valley" Constituent Terrain Features 24

Method 24

Results 26

Analysis I: PI (Clearings) 28

Method 28

Results 28

Effect of Interaction Between Route Geometry and 28

Terrain on Route Segment PS

Analysis J: Route Heading Change 28

Method 28

Results 28

Analysis K: Interaction of Heading Change and 32

Route Terrain

Method 32

Results 33

Effect of Combinations of PI ( ) and Local Terrain 34

Features on Route Segment PS

Analysis L: PI (1 Valley) and Various Local 34

Terrain Features

Method 34

Results 35 I

DISCUSSION OF THE ANALYSIS OF NIGHT 35

NAVIGATION DATA

Effect of Elevation Features on Interval PS 35

(Analysis A, B, and C)

Effect of Map Type on Interval PS (Analysis AB,C) 40

Effect of Hydrographic Features on Interval PS 40

(Analysis A, B, and C)

Effect of Vegetation Features on PS (Analysis D,E,I) 41

x
CONTENTS

Page

Effect of General Terrain Features on Route 43

Segment PS (Analysis F)

Significance of the Length Restriction of "I Valley" 44

(Analysis G)

Significance of "1 Valley" Constituent Terrain 44

Features on PS (Analysis H)

Effect of Route Heading Change on Route Segment 47

PS (Analysis H, J, K)

Effect of Terrain Classified by General Terrain Type 47

and by Local Terrain Features (Analysis L)

ROUTE AND SUBJECT SCORING 50

Method 50

Analysis of the Independence of Interval 50

Navigation Errors

Calculation of Route PS 50

Calculation of Subject Scores 51

Results 54

Independence of Interval Navigation Errors 54

Calculation of Route Probability of Success 55

Subject Scores 57

Discussion of the Subject Scoring System 57

Analysis of the Independence of Subject Errors 57

Calculation of Route PS 60

Subject Scoring Method 64

DAY NAVIGATION ANALYSIS 65

General Method 65

Analysis M: Calculation of the Probability of 74

Success PS in Intervals Classified by a Single

Terrain Feature Type

Method 74

Results 74

xi

A.it..... li . . . . 1
1

CONTENTS

Page

Analysis N: Prediction of Route PS as a Function 74

of the General Terrain Type Along the Route

Method 74

Results 74

Analysis 0: Effect of Route Geometry on 76

Interval PS

Method 76

Results 76

Discussion of Day Navigation Data 76

CONC LUSIONS 77

RECOMMENDATIONS 78

APPENDIX A 81

APPENDIX B 92

xii
LIST OF TABLES

Table Page

1 Listing of Terrain Features Used in Analysis 9

2 Pairs of Terrain Features That Did Not 11

Co-Exist in Any Interval

3 Significant Feature Categories - All Maps 13

4 Significant Feature Categories - NP-iC Map 14

5 Ps Per Interval Classified by Combinations 15

of Terrain Feature Type

6 Results of Regressions Using the Confusion Index 17

7 Terrain Features Providing Significant Prediction 18

of Route PS in a Univariate Linear Regression

8 Multivariate Linear Regression for Prediction 19

of Route PS

9 Univariate Linear Regression for One-Half 19

Route Segments: Elevation Features

10 Multivariate Linear Regression for One-Half 20

Route Segments: Elevation Features

11 Univariate Linear Regression for One-Quarter 22

Route Segments: Elevation Features

12 Multivariable Linear Regression for One-Quarter 22

Route Segments: Elevation Features

13 Comparison of "1 Valley" to "Salient Valley" 23

Based on Variance Explained in the Univariate

Regression Analysis

14 Variable Definitions 25

xiii
LIST OF TABLES

Table Page

15 Results of Correlation Analysis 27

16 Results of Correlation Analysis: Quarter Route 29

17 Results of Correlation Analysis: Half Route 30

18 Results of Correlation Analysis: Full Route 31

19 Results of Correlation and Regression Analysis 32

for Clearings

20 Significance of Heading Change Combined with 33

Terrain Feature

21 PS for Doubly Classified Route Intervals - 36

All Maps

22 PS for Doubly Classified Route Intervals - 37

Map NP-IC

23 PS for Doubly Classified Route Intervals - 38

Map AMD-3A

24 PS for Doubly Classified Route Intervals - 39

Map AMD-1 B

25 Analysis of Distribution of Number of Errors 54

Per Route

26 Effect of Average Interval PS on Route PS 56

27 Route Probability of Success 55

28 Subject Scores 58

29 Subject Average Scores and Ranking 59

xiv
LIST OF TABLES

Table Page

30 PS for Doubly Classified Route Intervals - 61

All Maps

31 PS for Night NOE Navigation 62

32 PS for Day Contour Navigation 63

33 Student Scoring Aid - Night Flight 66

34 Student Scoring Aid - Day Flight 67

35 Expanded Feature Classes With Notation 69

for Day Data

36 Differences Between Day and Night NOE Data 71

37 Matched Subjects - Day NOE 73

38 Regression Analysis Results 75

LIST OF FIGURES

Figure Page

1 Correlated Conditions 26

xv

* - - -- . .-
DATA ANALYSIS METHODOLOGY FOR DAY/NIGHT

INFLIGHT TACTICAL NAVIGATION

INTRODUCTION

Low-level helicopter navigation, both at night and during

the day, is an essential portion of the Army's mission. Navigation performance, defined as the probability of navigating along a

prescribed route or route segment without deviation, is a function

of many factors including map type, terrain type, and time of day.

Determination of the effects of these factors is important information for predicting performance of Army navigators, identifying

factors critical to navigation, evaluating the probability of navigation

success along a route and developing training aids. The purpose

of the research work reported here is to analyze low-level flight

experiment data to develop that i-nformation.

This report is the final technical report on contract

DAHC19-77-C-0042. It contains results from analyses of helicopter

low-level navigation data to determine the probability of navigation

success along a route as a function of terrain type, to develop a

means of determining route difficulty, and to develop a method of

scoring student performance. Documentation of the FORTRAN

computer programs and the navigation data base used in the analysis

are also included.

OBJECTIVES

The objectives of this research were as follows:

a. To develop an analysis methodology for inflight

tactical terrain navigation data;

b. To develop appropriate, documented computer

programs for analysis of existing and future data;

c. To compute validity/reliability coefficients of a

composite performance score; and

d. To develop a cardboard "slide rule-type" device

suitable for use by instructor pilots.

1
SUMMARY

Low-level navigation performance data were analyzed by

classifying the terrain along routes according to type and calculating

the probability of navigation success for each type. For this purpose,

routes were divided into 500 meter intervals and the terrain features

at or near each interval were recorded. Also recorded were the

subject navigation performance data at each interval. Subject performance data consisted of the route intervals attempted and the

route intervals successfully navigated. Computer files were

developed containing the interval terrain data and the subject performance data. A computer program was written to permit search

of these files and to determine the probability of navigation success

(Ps) in intervals classified by terrain type and other factors. PS'S

are computed for total routes and route intervals. PS is the

probability of navigation along the prespecified route or route

interval without error.

Route and student scoring techniques were developed where,

for the former, route PS is calculated as the product of route

interval PS's. Interval PS's have been determined as a function of

terrain type. Thus, with this method, PS can be predicted for new

routes. A student scoring method (SSM) was developed which weights

the score for each interval depending on the interval PS.

Two methods of classifying terrain were found to be

important for prediction of navigation success. One method uses

local factors such as a road, hill, valley, stream, etc. to describe

the terrain in or near a route interval. The other classification

type describes the general terrain in a larger area - a route segment

composed of multiple route intervals. The sequence lengths considered

included full, half, and quarter routes. With this classification method,

the "1proportion of intervals" (PI) where a specified terrain condition

exists was the variable used as an indication of general terrain type.

A convenient notation for this variable is PI) where the ( ) would

indicate the terrain conditions of interest. For example, PI (I valley)

means the proportion of intervals in a single valley.

Three classes of general terrain type were formed based

on the PI (I valley) (e.g., a route segment is classified as Class A

if 0 to 1/3 of the segment is in one valley, as Class B if 1/3 to

2/3 of the segment is in one valley, and Class C if greater than

2/3 of the segment is in one valley).

2
Salient terrain features resulting in either a significantly

high (or low) Ps of route intervals were identified along with the

associated PS values. Furthermore, it was determined that

differences in the general type of terrain result in differences in

the salient terrain features in intervals, e.g., for each class of

PI (1 valley) different terrain features, ponds, clearings, etc.

are important.

A significant increase in PS of intervals was found when

four or more elevation features are present (e.g., four or more

fingers, four or more hills, four or more draws). Another feature

typically associated with increased PS was two or more ponds.

Other features were also important depending on the general

terrain type.

Results also showed that ambient light level had no effect

on PS. Map type, however, had a significant effect with the

Experimental Night Photomap (NP-1C) proving a significant increase

in performance over map types AMD-3A and AMD-1B.

Another result showed that navigation errors along a

route can be considered as independent errors since the distribution

of numbers of errors per route (probability of exactly zero, one,

two, etc. errors) follows binomial distribution. This result permits

prediction of total route PS by the product of the route interval

Ps'S.

Based on the development of PS's as a function of terrain

type, a student scoring method was developed. This scoring system

normalizes the difficulty of the route, as measured by interval Ps ,

so that the expected score for an average student is 1 .00. The

standard deviation for the student population tested (using the three

types of maps referred to previously) is 0.05. The testing method

provides a means for scoring students against a norm - even on

new routes where experience data have not been collected.

GENERAL METHOD

Analyses were conducted using both night and day helicopter

navigation data. Night data were used to develop analysis and performance measurement techniques. When the technique development

was complete, it was evaluated using the day data.

3
The night navigation experiment employed 21 subjects

each flying four different routes. Each subject was a recent

graduate of the Initial Rotary Wing Program at Ft. Rucker,

Alabama. Three types of maps were used where each subject

used one type of map on all routes flown. Navigation performance

data consisted of maps marked with the reference route and the

actual flight path produced by the subject navigator. An instructor

pilot served as pilot and marked the subject's navigational errors

on the map.

Three types of experimental maps of the Petrey, Alabama,

area were used in the night navigation experiment. One map, with

white markings on a black background, was the Experimental Air

Movement Data (AMD) Red-light Night-Use Prototype #3A. This

map is referred to as AMD-3A. Another map with a white background was "AMD Experimental Prototype 1-B." This map is

h referred to as AMD-1B. Finally, a third map, "Experimental

Night Photomap #1C" had a black background with colored contour lines

and detail. This map is referred to as NP-1C.

The day navigation experiment design employed 10 routes

with 10 subjects flying each route. Different subjects were used

for each route requiring 100 total subjects. Data were collected

in the same way as for the night data. Two types of maps were

used - one type for five routes and the other type for the other

five routes.

One map used for the day data was a topographic map of

terrain near Ft. Rucker, Alabama, with contour intervals of 20

feet. This map is referred to as the "TM-20." The other map

type is a photomap base with 50 foot intervals. This map is

referred to as "PM-50."

For both night and day data, terrain features along each

route were identified by map inspection. To facilitate the analysis,

each route was divided into intervals 500 meters in length for

night data and 1,000 meters in length for day data. The terrain

features along and to the sides of each interval were recorded.

Terrain features within 1 ,000 meters of the route were coded.

This distance was assumed to be the limit of navigator visual

purview. Terrain feature types were identified in the following

categories:

4
Elevation Features

Ridge

Hill

Finger

Valley

Draw

Plateau

Vegetation Features

Dense Woods

Woods

Marsh

Clearing

Hydrographic Features

River

Stream

Intermittent Stream

Pond

Man-made Features

Highway

Road

Railroad

Buildings

Developed Areas

Bridges

Dams

Power Lines

In addition, parameters including location, size, altitude, shape,

and orientation were recorded. A complete description of the

terrain feature codes is presented in Appendix A.

Feature data were coded and entered into a computer file

for subsequent analysis. The coding technique used parameter

classification systems to categorize data and reduce the storage

requirements. For example, the location of each feature near an

interval was coded relative to the interval midpoint. The coding

system is called a "Star Code" which uses a polar coordinate

5
system with eight angular sections and three range rings. Thus,

a feature location such as the high point of a hill is identified by

an angular sector (e.g., 0-450) and range ring (e.g., 500 - 1,000

meters) with respect to the interval midpoint.

In addition to the route terrain file containing the terrain

feature codes, a subject file containing subject experiment trial

history was constructed. This file contains, for each subject and

each route attempted, those intervals not attempted (missed), and

the location of each navigation error. Intervals were missed for

several reasons including navigation errors and premature termination of the particular trial. Typically one or more intervals were

missed following an interval where a navigation error occurred -

the navigator returned to the reference route but skipped a portion

of the route. The portion of the route skipped consists of "missed"

intervals. Subject errors were defined as deviations from the

route exceeding 100 meters.

Analyses performed using the data files were classified

as follows:

Analysis of Night Navigation Data

I Effect of Local Terrain Features on Interval PS

Analysis A: Frequency of Joint Occurrence of

Local Terrain Features

Analysis B: Local Terrain Features Considered

Individually

Analysis C: Combinations of Te-rain Feature Type

Analysis D: Existence of Clearings in an Interval

Analysis E: Multiple Clearing Patterns Along

a Route

II Effect of General Terrain Features on Route

Segment PS

Analysis F: General Terrain Features

Analysis G: Evaluation of the Length Restriction

on "I Valley"

Analysis H: "1 Valley" Constituent Terrain Features

Analysis 1: PI (Clearings)

Li)

6
III Effect of Interaction Between Route Geometry and

Terrain Characteristics on Route Segment PS

Analysis J: Route Heading Change

Analysis K: Interaction of Heading Change and

Route Terrain

IV Effect of Combinations of PI ( ) and Local Terrain

Features on Route Segment PS

Analysis L: PI (1 Valley) and Various Local

Terrain Features

Development of a Route and Subject Scoring Method

Independence of Subject Errors

Calculation of Route PS

Calculation of Subject Scores

Analysis of Day Navigation Data

Analysis M: Calculation of the Probability of Success

(Ps) in intervals classified by a Single

Terrain Feature Type

Analysis N: Prediction of a Route Ps as a Function

of General Terrain Type Along the Route

Analysis 0: Calculation of Interval PS as a Function

of Route Geometry

NIGHT NAVIGATION ANALYSIS

EFFECT OF LOCAL TERRAIN FEATURES ON INTERVAL PS

Analysis A: Frequency of the Joint Occurrence of Features.

METHOD. Data files were searched to determine the

number of intervals in which each type of feature occurs. From

these results, an expanded set of variables was developed to further

categorize each feature type so that the effect on performance of

the number of features of a given type could be assessed.

The initial feature classification system used two categories:

"no features of a specified type present," and "one or more

7
features of a specified type present." Examples of this initial

classification system are "no ponds" and "one or more ponds."

The analysis permitted, for some features, further expansion of

the category "one or more features" into additional categories.

A typical example was the expansion of the feature classification

for fingers from "1 or more fingers" into "1, 2, 3 fingers," and

"4 or more fingers." The expanded feature classification was

selected so that each new cat9gory would occur in a large number

of intervals - thus providing a more detailed description of the

terrain feature than the original classification system. The expanded

set of features along with the number of intervals over all roules

in which the feature class exists are listed in Table 1.

Next, the number of intervals in which each combination

of two types of features occur was determined. For example, the

number of intervals in which ooth one or more ponds and one or

more roads occur was determined.

RESULTS. Results of Analysis A, the expanded set of

features, are shown in Table 1. In addition, Table 2 is a list of

combinations of features that never occurred together at the same

interval.

Analysis B: Local Terrain Features Considered Individually.

METHOD. Intervals were grouped according to the terrain

feature classification referred to above. A computer program was

written to enable search of the terrain feature and subject performance

files. Computer output from the search is the total number of subject

attempts and successes in the interval classes of interest. PS for

an interval class is then calculated as the number of successes

divided by the number of attempts at all intervals within that class.

Also, the PS and number of attempts and successes were sorted

automatically by the computer according to subject, route, and map.

A comparison of PS's was made for each pair of terrain

feature categories within a terrain type. For example, there are

three categories in the finger terrain type: "no fingers," "11, 2,

3 fingers," and "4 or more fingers." A PS was determined for

each category and PsIs were compared for each pair as follows:

No fingers vs. 1, 2, 3 fingers

No fingers vs. 4 or more fingers

1, 2, 3 fingers vs. 4 or more fingers

8
Table 1

LISTING OF TERRAIN FEATURES USED IN ANALYSIS

Number of*

Class Type of Number of Intervals

ID Terrain Features in Terrain Class

Number Feature Class Exists

1 Ridge None 75

2 Ridge 1 or more 57

3 Hill None 10

4 Hill 1, 2, or 3 79

5 Hill 4 or more 43

6 Finger None 13

7 Finger 1, 2, or 3 65

8 Finger 4 or more 54

9 Valley None 12

10 Valley 1 Only 63

11 Valley 2 or more 57

12 Draw None 17

13 Draw 1, 2, or 3 63

14 Draw 4 or more 52

15 Plateau None 109

16 Plateau 1 or more 23

17 Rivers None 37

18 Rivers 1 Only 73

19 Rivers 2 Only** 22

20 Stream None 7

21 Stream 1 or 2 69

22 Stream 3 or more 56

23 1. Stream None 21

24 I. Stream 1 or 2 77

25 1. Stream 3 or more 34

26 Pond None 67

*Number of intervals along all night routes is 132.

*More than two did not occur in any interval.

9
Table 1 (Concluded)

LISTING OF TERRAIN FEATURES USED IN ANALYSIS

Number of*

Class Type of Number of Intervals

ID Terrain Features in Terrain Class

Number Feature Class Exists

27 Pond 1 Only 45

28 Pond 2 or more 20

29 Highway None 81

30 Highway 1 or more 51

31 Road None 6

32 Road 1 or 2 66

33 Road 3 or more 60

34 Railroad None 117

35 Railroad 1 or more*** 15

36 Building None 67

37 Building 1 or more 65

38 Developed None 108

Area

39 Developed 1 or more*** 24

Area

40 Power line None 108

41 Power line 1 or more*** 24

42 Dam None 88

43 Dam 1 or more 44

44 Bridge None 82

45 Bridge 1 or more 50

*Nuirber of intervals along all night routes is 132.

***More than one never occurred in an interval. i

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I) C1 C 

0 0 L0

Vl . C.

O C h 0. h VL O.-

,4 C. , C- C

a 0 C. 0 011
Differences in PS that are statistically significant (observed

p : .10) are noted. A feature category with a statistically significant

improvement over another feature category is termed a "favorable"

feature. The other feature category is termed an "unfavorable" feature.

Wilcoxon tests and t tests were used to test PS's for

significance on the pairs of terrain categories. Both tests were

applied to a repeated measure experiment design since the subjects

performed in both categories of each pair tested. Tests were

conducted within a feature type such as "fingers." The pairings

tested for fingers were those listed in the previous paragraph.

PS data for certain feature classes might be considered

a small sample for two reasons. One reason is that few subjects

attempted intervals with that particular terrain classification. The

other reason is that only a few intervals along the routes were

associated with that terrain classification. Both the limited number

of attempts and limited number of intervals were noted in these

cases.

RESULTS. Results are summarized in Tables 3 and 4.

Table 3 shows the significant terrain feature categories, the

statistical significance (two tailed test), and the PS for both

feature types for data from all maps. In order to illustrate the

differences found with one map type from the aggregated data of

all map types, Table 4 shows results for map type NP-1C.

Analysis C: Combinations of Terrain Feature Types.

METHOD. The computer files were searched to determine

Ps at intervals classified by combinations of terrain feature types.

For example, PS for all intervals containing both "1 or more

ridges" and " 1 to 3 hills" were determined. Ps's for these intervals

were compared to the composite Ps. The composite PS is the PS

for all intervals regardless of terrain type. This comparison of

deviation from the composite PS was computed on a subject-bysubject basis yielding 21 subject samples. The PS's were tested

for significant differences from the composite PS using the t test.

RESULTS. Results of Analysis C, the calculation of PS

for combinations of route features, are presented in Table 5.

The significant feature classifications had an observed t statistic >

t .10 (20) for a one tailed test.

12
OD

OD

C

0.o~

~.1

4 1

4-,N

0 O __ 0 - 0 C

-U 03 0 0 0 0 0 C

I CI0 * 100' 0 0 0

0 4

U)) E) > 0 )C

L -

(d 45

>

L4-,

L" .- (- (-

D 0 0 00 3 0 4

o C. L L .- 

0 0H L D_ ' , ii. U) - 4) 0 -0 .'C.0 0

0 0 ft

(0 CO (n W (n4 0L . >) >. > 0 > > . > . > ) > 0

0. 1 ~ o~- ~ . C V . C 0)

1)34

.. 00 L

z~~ (0m(

0~~ L ~ 0LC

IL U) 4)

", C .C C

U) U) 0

0L 0 0 4) c . 4

L L L L >-( (0 0 0 C C 0 0. w 0 0. ~ a ~ C ~ ( 0.a 

L E) E E) E) E 4 E L E

0. (d 4 0 >3

L C. L C. C. C. > ) C. -( 0.a 0) o 0 0 0 0 0 0 0 0 > 0 (

L

13
4-.0

L) -o

A. 1 0[

U 0 t 0 0 n •

z -- 0 - 2 1

Uw n

COL 05

14)

0 0... to.. . ..

0 It.

0 L 0D 0) L 0 >- 0 0) 0 0 0

D' 0I t U) ( L' 0 o 2

144
Table 5

PS PER INTERVAL CLASSIFIED BY COMBINATIONS

OF TERRAIN FEATURE TYPE

p One Tailed

t-Test

Feature Class df = 20 PS

No ridge, 1 or more highways, .01 .971

3 or more roads

No ridge, 4 hills .01 .969

1 or more highways, 3 or more

roads, no railroad .01 .969

No ridge, 4 or more fingers .02 .958

4 or more hills, 4 or more fingers, .05 .958

4 or more draws

4 or more hills, 1 to 3 fingers, .02 .958

1 to 3 draws

4 or more fingers, 4 or more draws .02 .957

1 to 3 hills, 4 or more fingers .05 .956

No ridge, 3 or more roads .02 .956

1 or more ridges, 4 or more hills, .10 .955

i to 3 draws

No ridge, 4 or more draws .05 .954

I or more ridges, 4 or" more fingers .10 .952

4 or more hills, 4 or more fingers .10 .952

1 or more ridges, 3 or 4 hills .10 .950

4 or more hills, 1 to 3 draws .05 .950

15
The highest probability of success was a .971 the category

"no ridge, 1 or more highways, 3 or more roads." Other categories

yielding superior PS of .969 were "1 or more highways, 3 or

more roads, no railroad," and "no ridge, 4 hills."

Analysis D: Existence of Clearings in an Interval.

METHOD. The relationship between the existence of one

or more clearings in an interval and PS of that interval was studied

by comparing the PS of intervals with clearings to the PS of intervals without clearings. Since each subject navigated along

intervals both with and without clearings, a difference in PS was

calculated for each subject. A t-test for a repeated measure

statistical analysis was used to evaluate the significance of the

Ps differences.

RESULTS. Since the observed t was less than t .10 (20),

results of the analysis showed no significant relationship between

the existence of one or more clearings in an interval (within 500

meters of the interval) and Ps of that interval.

Analysis E: Multiple Clearing Patterns Along a Route

METHOD. The clearing patterns along a route may

influence the Ps. Therefore an index termed the "confusion index"

was defined based on the number of changes of vegetation to clearings

or clearings to vegetation. Two measures used were the number of

changes 300 and 600 meters from the route and were identified as

T 3 and T 6 respectively. -The index was taken as the ratio T 3 /T 6 .

This index measures the number of short indentations of clearings

along the route which might result in a navigator's confusion in

distinguishing separated clearings from connected clearings.

Data were collected along the route 2,000 meters prior to

each abrupt route turn. A navigation error for that turn was defined

as any flight error exceeding 100 meter deviation from the route

within the 2,000 meter interval. With PS as the independent

variable, regressions were run using the ratio T 3 /T 6 and that

ratio weighted by the number T 3 , i.e., T 3 x T 3 /7 6 in univariate

regressions, and both T 3/T 6 and T 3

2 /T 6 in a multivariate regression.

RESULTS. Results are shown in Table 6. The univariate

regressions using Ta/T_ and T 3

2 /T 6 as independent variables are

not significant. But the multivariate regression is significant

F (1,10) = 4.83, p < .05.

16 I.
Table 6

RESULTS OF REGRESSIONS USING THE CONFUSION INDEX

Independent Regression F

Variable(s) Coefficient Statistic df p

Ta/T .25 1,10 NS

T 2/T 1.08 1,10 NS 3 6

T3/T6 .464 '

3 6 and 4.83 2,9 .05

T32/T -.0337

EFFECT OF GENERAL TERRAIN FEATURES ON ROUTE SEGMENT PS

Analysis F: General Terrain Features.

METHOD. Regression analyses were conducted using as

independent variables "the proportion of intervals" (PI) along a route

associated with specified terrain features. The dependent variable

was the probability (PS) of completing the route without navigational

error. As an example, the variable PI (1 valley) which is the

proportion of intervals along a route that are associated with "1

valley," was used to predict probability of success for that route.

Since there are only four routes, only four data sets are

available for each regression analysis. With these limited data,

only one or two independent variables could be used in the regression.

However, additional tests were run to predict probability of success

of one-half and one-quarter routes. This not only tests the power

of the regression to predict performance on shorter route segments,

but also makes additional data sets available. Thus, for one-half

and one-quarter route regressions, eight and 16 data sets are

available respectively. As a result, multiple independent variables

were used in those regressions.

17
The first step in the analysis was to examine each of the

45 (expanded set of) terrain features to identify those with significant

prediction of route PS. Second, combinations of features were tested

for multi-variable prediction of route and route segment PS. F tests

were used to test the statistical significance of the variance explained

by each regression.

The effect of ambient light level, as measured by a photometer, was also tested. This test was to determine if there is a

relationship between ambient light level and navigation performance.

A regression analysis was conducted using the route PS as the

dependent variable and light level as the independent variable.

Data for the test were from the 19 subjects who performed on

route one. p

RESULTS. Results of the univariate linear regression

analysis are given in Table 7 which provides a list of single terrain

features that can reliably predict route PS. Also listed is p - the

probability that the results could have been achieved by chance.

'One valley only" was a significant predictor of route PS with a 3

positive correlation. Features "1 to 3 fingers," "2 or more

valleys" and "1 to 3 draws" also provided significant prediction of

PS but with negative correlations, i.e., a decrease in feature

frequency is associated with an increase in route PS.

Table 8 gives the results of multiple linear regression

tests. Two combinations of variables provided significant prediction

of PS: "1 to 3 fingers, 2 or more ponds" and "1 valley, 1 or

more plateaus.

Table 7

TERRAIN FEATURES PROVIDING SIGNIFICANT PREDICTION

OF ROUTE PS IN A UNIVARIATE LINEAR REGRESSION

Feature Class R F Statistic P ( df = (1 ,2)

1 to 3 Fingers -.920 11.1 .073

1 Valley Only +.967 30.1 .031

2 or More Valleys -.944 16.5 .055

1 to 3 Draws -.952 19.6 .047

18
Table 8

MULTIVARIATE LINEAR REGRESSION FOR

PREDICTION OF ROUTE PS

Feature Class R F Statistic P

df = (1,2)

1 to 3 Fingers, 2 or .999 881.2 .023

more Ponds

1 Valley, 1 or

more Plateaus .997 119.3 .064

Tables 9 and 10 give regression results for predicting PS

over half-route segments. Table 9 presents the results for univariate

regressions and reveals that valley, draw, and finger feature classifications provide significant predictions. Valley classification "1

valley" and "2 or more valleys" can account for over 70 percent of

the one-half route PS variance.

Table 9

UNIVARIATE LINEAR REGRESSION FOR ONE-HALF

ROUTE SEGMENTS: ELEVATION FEATURES

Feature Class R F Statistic df = (1,6)

1 Valley +.871 18.9 .005

2 or More Valleys -. 856 16.6 .01

4 or More Draws +.777 9.1 .05

1, 2, 3 Draws -.728 6.7 .05

4 or More Fingers +.675 5.0 .10

19
Table 10

MULTIVARIATE LINEAR REGRESSION FOR ONE-HALF

ROUTE SEGMENTS: ELEVATION FEATURES

F P,

Feature Class R Statistic df = (2,5)

1, 2, 3 fingers, 1 valley .960 30.1 .002

No plateau, 1 valley .957 23.9 .005

1 plateau, 1 valley .951 23.8 .005

2 or more valleys, 1, 2, or 3 fingers .932 16.9 .01

2 or more valleys, 1 or more plateaus .906 11.6 .025

2 or more valleys, no plateau .906 11.6 .025

1 valley, 4 or more draws .884 9.0 .025

4 or more hills, 4 or more draws .882 8.8 .025

4 or more hills, 2 or more valleys .874 8.1 .05

1, 2, 3 hills, 1 valley .872 8.0 .05

1, 2, 3 hills, 2 or more valleys .866 7.5 .05

1, 2, 3 hills, 4 or more draws .863 7.3 .05

4 or more hills, 1 valley .872 8.0 .05

1, 2, 3 fingers, no plateau .854 6.8 .05

1, 2, 3 fingers, 1 or more plateaus .854 6.8 .05

4 or more fingers, 1 valley .871 7.9 .05

4 or more fingers, 2 or more valleys .859 7.0 .05

No valleys, 4 or more draws .843 6.2 .05

1 valley, 1, 2, 3 draws .871 7.9 .05

2 or more valleys, 1, 2, 3 draws .858 7.0 .05

2 or more valleys, 4 or more draws .857 6.9 .05

4 or more hills, 1, 2, 3 draws .837 5.9 .05 )

2Q
Table 10 gives the results of the multivariate regression

for one-half route segments. Again, valley classifications in

combination with "1, 2, 3 fingers," "no plateau," "I plateau"

provide significant prediction and account for over 80 percent of

the one-half route PS variance.

Results of regression for one-quarter route PS prediction

are listed in Tables 11 and 12 for univariate and multivariate analyses

respectively. For one-quarter route PS prediction, fingers, draws,

hills, and plateaus appear to be more significant than valleys;

however, the proportion of variance explained is approximately 20

percent for the univariate analysis and less than 40 percent for

the multivariate analysis.

Results of the prediction of route PS as a linear function

of light level showed that light level is not a significant prediction

of route PS"

Analysis G: Evaluation of the Length Restriction on "I Valley".

METHOD. Prediction of probability of success (PS) for full

routes and route segments demonstrates that performance is, in part,

a function of general terrain type, and that general terrain type can

be characterized by PI (1 valley). However, as shown in Tables 7,

9, and 11, the variance explained by a univariate regression (employing P1 (1 valley) as the independent variable) decreases as the length

of a route segment decreases. This may be due, in part, to the

definition of 1 valley which specifies that the length of 1 valley

exceed 2,500 meters. But, the length of a one-quarter route segment

is also approximately 2,500 meters. Thus, the lower length limit in

the definition of 1 valley may prohibit accurate prediction of PS for

quarter-route segments. In order to determine if this lower bound

on length causes the reduction in variance explained, a new definition

for a single valley was formed and used in a regression analysis to

predict route and interval PS. This type of single valley, which

does not involve a length limit, is termed "salient valley," and is

defined as follows:

An interval is classified as containing a salient valley

when all of the following conditions are met:

1. The specified route is in a valley over the

length of the interval.

21
Table 11

UNIVARIATE LINEAR REGRESSION FOR ONE-QUARTER

ROUTE SEGMENTS: ELEVATION FEATURES

F P,

Feature Class R Statistic df = (1,14)

4 or more draws .512 5.0 .05

No plateau .446 3.5 .10

1 or more plateaus .446 3.5 .10

2 or more valleys .432 3.2 .10

Table 12

MULTIVARIATE LINEAR REGRESSION FOR

ONE-QUARTER ROUTE SEGMENTS: ELEVATION FEATURES

F p,

Feature Class R Statistic df= (2,13)

1, 2, 3 fingers, 4 or more draws .627 4.2 .05

1, 2, 3 fingers, 2 or more valleys .600 3.7 .10

4 or more hills, 4 or more draws .574 3.2 .10

1, 2, 3 hills, 4 or more draws .562 3.0 .10

No plateaus, 4 or more draws .560 3.0 .10

1 or more plateaus, 4 or more draws .560 3.0 .10

22
2. All other terrain depressions (if any) within

1,000 meters of the route have a floor slope

greater than 100 feet per 1,000 meters, or have

a floor that rises to the horizon.

3. The width between the "high points" to either

side of the valley is less than 2,000 meters.

4. The change of route path elevation is less than

60 feet over the length of an interval (500

meters).

Terrain along each route was classified at each interval

according to the salient valley definition. A regression analysis

was conducted using the "proportion of intervals containing a

salient valley" (PI (salient valley) ) as the independent variable

and PS as the dependent variable. Regressions for segment

lengths of 1 route, 1/2 route, and 1/4 route were calculated and

the variance explained by the PI (salient valley) regressions was

compared to that of PI (1 valley) regressions.

RESULTS. Comparison of the regression analysis using

"1 valley" and "salient valley," in terms of variance explained,

is shown in Table 13. It is clear that the PS prediction capability

of the "salient valley" classification decreases as the lengths of

the route segment decreases, and that the salient valley prediction

explains less variance for full and half routes than does the "I

valley" classification.

Table 13

COMPARISON OF "1 VALLEY" TO "SALIENT VALLEY"

BASED ON VARIANCE EXPLAINED IN THE

UNIVARIATE REGRESSION ANALYSIS

"I Valley" "Salient Valley"

R R 2 R R 2

Full Route .968 .938 .760 .578

Half Route .872 .760 .561 .314

Quarter Route .401 .161 .468 .217

23
IF.I

Analysis H: "1 Valley" Constituent Terrain Features.

Comparative analysis of the performance prediction

capability of the P1 (1 valley) and PI (salient valley) variables led

to the understanding that that type of terrain is identified by several

constituent terrain and route conditions. Since one or more of

these conditions may be as important to navigation performance as

the aggregate "1 valley" classification, performance relationships

of each condition and combinations of conditions were investigated.

The four conditions listed in the previous section as the definition

components for a salient valley were used and are repeated in

Table 14 for reference. Note that a symbol is defined in the table

to represent each condition, e.g., VT true means "it is true that

the route follows a single valley;" obviously, VF false means that

the route is not in the valley. In addition to the four conditions

previously described, a fifth condition, route heading change was

investigated and is listed in the table.

METHOD. First, correlation analyses were conducted with

the five factors referenced above for full route, half route, and quarter

route navigation performance (PS). The variables measuring each condition for the correlation analysis were the proportion of intervals (PI)

along the route segment at which each condition is true. A Pearson productmoment correlation (r) coefficient was determined for each correlation.

A test of the null-hypothesis that r = 0 was applied to the correlation results.

Next, the performance prediction of each of the five

conditions and combinations of conditions was investigated using a

univariate regression analysis. In this analysis, the independent

variable representing each condition or combination of conditions

was the proportion of intervals in which a specified logical function

of the conditions is true. For example, one logical combination

of the conditions is:

X, V D .N .E H 1 T T T T T

where "." is the logical AND operation.

Thus, the variable X 1 is true only when all of the constituent

factors are true. The corresponding independent variable used in

the regression analysis is the portion of intervals at which X 1 is

true, i.e., PI (X 1 ). Consider another example:

X =V D N .E H 2 T T T T F

According to this equation, X 2 is true only when all the factors

specified are true; note, however, that (differing from the previous

24
Table 14

VARIABLE DEFINITIONS

VT Route follows a valley over

length of interval.

DT No depression (other than the

depression the route follows) rises

less than 100 feet (or to the elevation

of high features) over 1,000 meters

to either side of the interval.

N T The depression which the route follows

h is less than 2,000 meters in width

to the bounding linear elevation to

either side of the interval.

E Route rises less than 60 feet over

the length of interval.

HT Heading changes less than 100 over

length of interval.

equation) HF* is true only when heading change is not less than

100 over the length of the interval. A further example which

illustrates yet another type of logic function is:

X 3= V 7 0 N T ET X3 =VT • T T • T

In this example, the heading condition is not specified so that the

logic "does not care" if the heading condition is true or false.

The performance prediction analysis used all combinations

of the five conditions taken five at a time, four at a time, three at

a time, two at a time and each individual condition. Three sets of

regression analyses were conducted using the dependent variable

(PS): full route, half route, and quarter route respectively.

*H is the logical inverse of H so that when H is true H is

F T T F false and visa versa.

25
RESULTS. Results of the correlation analysis are given

in Table 15. As shown in the table, the significance of the correlation

varies with route segment length. Recall that the amount of data

available increases for decreasing length of the route segment, i.e.,

4, 8, 16 samples for full, half, and quarter route segments

respectively. Figure 1 presents a different organization of the data,

where VT, NF, ET are shown to be positively correlated.

NF is the logical inverse of NT, i.e., NF true means that the

valley width (distance between the high points) exceeds 2,000 meters.

Each of these three factors is negatively correlated with performance.

Figure 1

CORRELATED CONDITIONS

VT + _ F 1 T 

PS

Results of Table 15 show that the VT factor (specifying

that the route is in a single valled/) taken as a single factor, is

highly correlated with performance (PS) for half and full routes.

But, the NF factor (specifying that the width of the valley is

greater than 2,000 meters) has a larger correlation with PS for

quarter routes and the correlation is almost as large for half and

full routes. Since the ET factor (specifying a gradual slope of the

valley floor) is highly correlated with N and V, buc does not have

as large a correlation with performance as does N and V, the E

factor was eliminated in the regression studies. By the same

reasoning, either N or F could have been eliminated since they

are correlated; however, since one is not superior for predicting

P. for all route segment lengths, both were used in the regression

analysis.

26
Table 15

RESULTS OF CORRELATION ANALYSIS

Pearson Product-Moment

Correlation Coefficient r

Route Length

Condition Quarter Half Full

Combinations df = 14 df = 6 df = 2

V TODT -. 251 -. 119 -. 547

V TN T-575** -709** -. 9O8*

V TE T.807*** .674* .843

V TH T 494* .132 -. 239

vT PS-.111 -753** -. 982**

0T NT .414 .484 .1

D E 38*599 -. -434

T T

D T HT .070 -. 346 .757

DT P .013 .352 .6

NT ET -. 572** -. 773**93

N TH T-4.29 -. 548 -. 190

N P .504* .771* .812

T S

T H .304 .576 .155

E TP S-.033 -. 881 -. 759

HT P .225 .074 .419

*p .10 H:0 r= 0

** .05

**p ! .01 H: r pi0

27
Tables 16, 17, and 18 provide results of the univariate

regression analyses using as the independent variables the logical

combinations of the five terrain and route factors described above.

Analysis I: PI (Clearings).

METHOD. A correlation analysis was performed to

determine the Pearson product-moment correlation coefficient (r)

for the "proportion of route intervals with one or more clearings"

and route segment PS. Route segment lengths for full route, half

route and quarter route were used.

In an additional analysis, a multivariate regression was

used to determine the effect of combinations of clearings and a

specified terrain characteristic on route segment performance.

For the analysis, one independent variable was the "proportion of

route intervals with one or more clearings" while the other

independent variable was the proportion of route intervals in

"1 valley."

RESULTS. Table 19 provides the results of the correlation analysis

and reveals that the correlation between the "proportion of route

intervals with clearings" and route segment PS is small. Table 19

also provides the results of the multivariate regression analysis.

Results of univariate regressions previously reported which use

terrain characteristics as the independent variable are also presented

so that the variance explained with and without clearings can be

compared.

EFFECT OF INTERACTION BETWEEN ROUTE GEOMETRY AND

TERRAIN ON ROUTE SEGMENT PS

Analysis J: Route Heading Change.

METHOD. The effect of route heading change on interval

Ps was tested with a repeated measure sign test. For this test,

a route heading change was deemed to occur if the route heading

change over a 1 ,000 meter interval exceeded 10.O PS for each

subject for intervals with a heading change was compared to that for

intervals without a heading change. The sign test was used to

evaluate the statistical significance of the result.

RESULTS. No significant relationship was found to exist

between interval PS and heading change.

28
Table 16

RESULTS OF CORRELATION ANALYSIS: QUARTER ROUTE

p*

Condition 2 df -14

Combinations PI ( )r n = 1

N H .416 .645 .416 .001 T T

NFHF .077 -. 456 .208 .01

NFHT .113 -. 444 197 .01

NTH F NS

N D .181 -. 570 .325 .01

F F

N D .208 .555 .308 .01

T F

V and D, NS

V and H

combinations

N H D .054 -. 501 .251 .01

NT H FD F .083 .435 .189 .1

NFHTDF .127 -. 478 .228 .1

NT H TD F .125 .527 .278 .01

NT H TD T .292 .440 .193 .1

Other combinations

of N,H,D NS

Combinations of NS

V,H,D

PI( is the proportion of intervals where the combination is true.

Reference Sokal, R. R., Rohlf, F. J., Biometry, W. A. Freeman

& Company, 1969, pp. 516-518.

29
Table 17

RESULTS OF CORRELATION ANALYSIS: HALF ROUTE

p *

Condition 2 lf= 6

Combinations PI ( ) r r n = 1

NTHT .417 .744 .553 .01

NTF .219 .. 679 .461 .1

N F HF .078 -. 899 .808 .001

NFHT NS

NFD F .180 -. 851 .724 .001

NT DF .210 .775 .600 .01

Other combinations NS

of ND

VFHT .122 .736 .541 .01

Other combinations NS

of V,H

VF DT .141 .718 .515 .01

Other combinations NS

of VD

T HT DT .290 .711 .505 .01

T HT F .127 .689 .474 .1

NT HF DF .084 .63 .397 .1

NFHTDF .127 -. 701 .491 .1

NTHFDF .054 -. 784 .615 .01

Other combinations NS

of V,H

PI () is the proportion of intervals where the combination is true.

Sokal, op. cit., pp. 516-518.

30
I

Table 18

RESULTS OF CORRELATION ANALYSIS:

p*

Condition 2 df= 6

Combinations PI ( ) r r n = 1

NF HF .078 -. 974 .949 .01

Other combinations NS

of N,H

NFDF .179 -. 961 .924 .01

Other combinations NS

of N, D

VF F .097 .929 .863 .1

Other combinations NS

of FH

NTHTD T .293 .903 .815 .1

N H D .054 -. 948 .898 .1

Other combinations NS

of N, HD

V H D .099 .999 .998 .001

Other combinations NS

of FH,D

PI ( ) is the proportion of intervals where the combination is true.

Sokal, op. cit., pp. 516-518.

31
Table 19

RESULTS OF CORRELATION AND REGRESSION

ANALYSIS FOR CLEARINGS

Correlation between route segment PS and proportion

of route intervals with a clearing.

Route Segment r

Full Route .262

Half Route .303

Quarter Route .106

PS variance (R 2 ) explained by the proportion of route

intervals with a clearing and PI (1 valley).

Univariate

Regression Multivariate

(terrain Regression

Terrain Segment characteristic (terrain and

Characteristics Length only) clearing)

1 Valley Full Route .937 .956

Analysis K: Interaction of Heading Change and Route Terrain.

METHOD. The effect of heading change and route terrain

on route, half route, and quarter route PS was evaluated with a

multivariate regression analysis. Several regression analyses were

conducted using PI (heading change) for one independent variable

and PI (specified terrain characteristics) for the other independent

variable. The specified terrain characteristics were those previously

found to be related to PS.

32
RESULTS. Results of the regressfon analysis are presented

in Table 20. The results of univariate regression analyses using the

specified terrain characteristics as the independent variable are also

included in the table. This aids in identifying the additional variance

(if any) explained by the introduction of heading change information

into the regression.

Table 20

SIGNIFICANCE OF HEADING CHANGE

COMBINED WITH TERRAIN FEATURE

Univariate S ignificance

Regression Multivariate of

(terrain Regression with Multivariate

Feature Characteristic Rnly) Heading Change Regression

Class R R R Re p

Effect of Heading Change on Full Route Prediction

1 valley .967 .937 .972 .946 NS

1 - 3 draws -. 952 .907 .993 .986 NS

2 or more -. 944 .891 .992 .983 NS

valleys

1-3 fingers -. 920 .847 .996 .993 .10

Effect on Heading Change on Half Route Predtction

1 valley .871 .759 .886 .784 .025

2 or more -.856 .734 .885 .783 .025

valleys

4 or more .777 .605 .816 .666 .10

draws

Effect of Heading Change on Quarter Route Prediction

4 or more .512 .263 .691 .477 .025

draws

1 or more -.446 .199 .602 .363 .10

plateaus

2 or more -.432 .187 .537 .288 NS

valleys

Correlation between heading change and PS is -. 415 (full route)

33
EFFECT OF COMBINATIONS OF PI ()AND LOCAL TERRAIN

FEATURES ON ROUTE SEGMENT PS

Analysis L: PI (1 Valley) and Various Local Terrain

Features.

METHOD. This analysis employed two ways of classifying

intervals. First, groups of intervals were classified by the proportion of intervals in which "1 valley" occurred (PI (1 valley) ).

The value of PI (1 valley) was classified into three categories as

follows:

Proportion of Intervals Associated

Category With One Valley

A PI (1 valley) = .00 - .33

B PI (1 valley) = .34 - .66

C PI (1 valley) = .67 - 1.00

One-quarter route segments were classified as shown above. This

provides three categories according to general type of terrain over

several route segments, i.e., successive intervals. Rationale for

this additional classification is that navigation performance may be

a function of general terrain type over several successive intervals

and the specific terrain type in intervals.

Intervals classified in Categories A, B or C according to

the value of PI (1 valley) were further classified by terrain type

and the associated PS was determined by computer search. Again,

t-tests tests were applied to determine the significance of PS

for each type of terrain. For these statistical tests, the PS for

the doubly classified intervals (i.e.; classified by general terrain

type (PI (1 valley) and specific terrain in intervals) were compared

to the composite PS for all intervals. Within each PI (1 valley)

category, i.e., for each class of general terrain, terrain features

that result in an exceptionally high or low interval PS are noted.

An exceptionally high (favorable) or low (unfavorable) PS is one

that is significantly different statistically from the PS for all i

intervals.

34
RESULTS. Calculation of PS for intervals classified

by general terrain type along a route segment and also classified

by terrain type in an interval, resulted in the data presented in

Tables 21, 22, 23, and 24. Results presented are the intervals

with a significantly different PS from that computed over all

intervals, i.e., the composite PS. Results are organized by

each PI (I valley) class.

DISCUSSION OF THE ANALYSIS OF

NIGHT NAVIGATION DATA

EFFECT OF ELEVATION FEATURES ON INTERVAL PS

(ANALYSIS A, B, AND C)

Results show that elevation features which include "4 or

more fingers," "no valleys," "I valley," "4 or more draws," "no

plateau" are significantly related to navigation performance. Comparison

between "4 or more fingers" versus "no fingers" shows that a

substantial increase in the interval PS is obtained with 4 or

more fingers (.936), and a substantial decrease is obtained for intervals

where no fingers are present (.894). These results show that

superior performance for all maps is obtained where multiple

fingers and draws exist. This suggests that terrain ruggedness

increases the potential for the existence of unique terrain features

which can be useful terrain cues for the navigation.

The increase in interval PS with "I valley" over both

"no valleys" and "2 or more valleys" suggests that while ruggedness

of terrain and a single valley path are of value to navigation, the

existence of more than one valley may lead to navigation errors

because of the alternative routes present. Performance differences

associated with valley classifications are not just interval oriented,

but suggest a way of classifying the general terrain along a route

segment consisting of multiple intervals in sequence. This concept

of characterizing the general terrain was considered in Analyses

F, G, H, and I.

The improved performance where there is "no plateau"

can be interpreted by observing that the existence of a single plateau

means that an elevated and generally low lying area of limited features

is present immediately adjacent to the route. This lack of salient

features may lead to the low probability of success where routes

pass near plateaus.

35
Table 21

PS FOR DOUBLY CLASSIFIED ROUTE INTERVALS

ALL MAPS

Features With p

S ignificantly t-test Favorable Unfavorable

Different PS df = 20 Feature Feature

4 or more fingers .01 .980*

No bridge .02 .899

1 or more buildings .05 .894

No draw .01 .886

No fingers .05 .878

PS for all other features S! is .976 (18.7% of Class A intervals)

PI (1 valley) - Class A

No stream .001 1.000

1 or more railroads .001 1.000

2 or more ponds .001 .983

4 or more hills .001 .979

1 or more bridges .001 .969

1 or more dams .01 .962

PS for all other features is .883 (14.6% of Class B intervals)

PI (1 valley) - Class B

2 or more ponds .001 .988

1 or more dams .001 .983

PS for all other features is .931 (83% of Class C intervals)

PI (1 valley) - Class C

*Only five intervals

Note: P over all intervals without regard to terrain features

is .936.

36
Table 22

PS FOR DOUBLY CLASSIFIED ROUTE INTERVALS

MAP NP-1C

Features With p

Significantly t-test Favorable Unfavorable

Different PS df = 5 Feature Feature

None

PS for all features is .973

PI (1 valley) - Class A

4 or more hills .02 1.00

No stream .02 1.00

2 or more ponds .02 1.00

1 or more railroads .02 1.00

PS for all other features is .949 (31.7% of Class B intervals)

PI (1 valley) - Class B

I pond .02 1.00

1 or more dams .02 1.00

No ponds .10 .946

One or more of the above features are present at all

Class C intervals

PI (1 valley) - Class C

Note: PS for subjects using Map NP-IC over all intervals without

regard to terrain features is .968.

37
Table 23

P FOR DOUBLY CLASSIFIED ROUTE INTERVALS

MAP AMD-3A

Features With p

Significantly t-test Favorable Unfavorable

Different PS df = 6 Feature Feature

No bridges .05 .853

1 or more dams .10 .847

PI (1 valley) -Class A

1 or more railroads .001 1.000

No stream .001 1.000

2 or more ponds .02 .984

1 or more highways .01 .983

1 or more bridges .05 .971

1 or more dams .01 .966

1 pond .01 .966

1 or more ridges .10 .965

1 to 3 fingers .02 .963

4 or more hills .10 .962

PI (1 valley) -Class B

1 or more dams .05 .979

No stream .10 .872

PI (1 valley) - Class C

Note: PS for subjects using Map AMD-3A over all intervals S(

without regard to terrain features is .919.

38
Table 24

PS FOR DOUBLY CLASSIFIED ROUTE INTERVALS

MAP AMD-1B

Features With p

Significantly t-test Favorable Unfavorable

Different PS df = 7 Feature Feature

No bridges .10 .887

1 or more buildings .10 .873

1 to 3 fingers .10 .866

1 or more highways .05 .863

PI (1 valley) - Class A

I or more railroads .001 1.000

No streams .001 1.000

4 or more hills .020 .977

1 or more hills .001 .970

2 or more ponds .05 .970

1 or more dams .05 .942

P[ (1 valley) - Class B

2 or more ponds .001 1.000

3 or more streams .01 .981

1 or more dams .05 .975

4 or more fingers .02 .965

4 or more draws .1 .963

No ridge .1 .957

1 to 3 hills .1 .956

No buildings .1 .952

PI (1 valley) - Class C

Note: PS for subjects using Map AMD-1B over all intervals

without regard to terrain features is .928.

39
EFFECT OF MAP TYPE ON INTERVAL PS (ANALYSIS A, B, C)

Comparing the results of all maps with the results of the

NP-IC map for the terrain category "no hills" illustrates the

superior performance obtained with the NP-1C map even though

the category "no hills" occurred at just 10 intervals over all four

routes. These areas consisted of a low wide valley where the

specified routes were along a relatively straight path and dead

reckoning could be used for navigation. This type of terrain lacks

salient navigation features, yet inspite of this, resulted in very

superior performance for the subjects using the NP-IC map. The

fact that performance over all maps did not show this elevation

category as superior indicates that with the other two types of

maps (AMD-3A, AMD-1B) performance must have been poor. It

must be concluded that some differences in the NP-iC map permits

successful navigation in terrain classified as "no hills." Similar

results occurred with the elevation category "no draws" where

subjects using the NP-IC map provided superior performance. For

all maps, man-made features "one or more railroads," "one or

more bridges" provide significant improvement of performance,

presumably because these features are unique cues.

It is found that bridges are consistently important cues

with the NP-1C map even though bridges are not coded on this

type of map. Apparently, the existence of a bridge can be

reliably assumed (i.e., from a road crossing a stream) by the

navigator; the bridge symbol is not required.

EFFECT OF HYDROGRAPHIC FEATURES ON INTERVAL PS

(ANALYSIS A, B, AND C)

Results shown in Table 3 reveal that the category "2 or

more ponds" is a very significant factor in navigation performance

over ??no pond" and "1 pond." Intervals associated with two or

more ponds provide a .986 probability of success per interval.

Apparently, two or more ponds provide a higher likelihood of the

unique pattern than does one pond or other terrain features.

Table 4, which gives the results for subjects using the

NP-1C map, shows a similar result of the pond for "2 or more

ponds" revealing that ponds are an important navigation clue

regardless of the type of map used.

The result which indicates that "no stream" is superior

to "I or more streams" is curious since one would expect streams

40

4
would provide good terrain-following cues. However, analysis of

this result shows that frequently associated with the "no stream"

category are rivers, valleys, and draws which are excellent

terrain-following cues.

EFFECT OF VEGETATION FEATURES ON PS (ANALYSIS D, E, AND I)

According to the results of Analyses D and 1, the Ps's for

intervals with clearings (and also those without clearings) were not

found to be significantly different from the composite PS. Based

on this result, there is no support for believing there is a relationship between the "existence of one or more clearings in an

interval" and navigation performance in that interval.

In contrast, both experienced and student pilots state that

clearings 6re an important and often used cue for navigation.

Since in the analysis the measure of a clearing was simply its

existence without regard to size, shape, or number of clearings

and without regard to the uniqueness of the clearing shape with

respect to other clearings in the area, failure to detect a

significance relationship with performance may be due to the

simplified method of coding clearings.

The shape of clearings was considered in Analysis I -

particularly with regard to the fidelity of the shape of clearings

depicted on a map. (It is recognized, of course, that the shape of

actual clearings is not constant and can change with season of the

year, construction, and farming practices.) But, the actual

clearings were not at hand so it was reasoned that comparison of

clearing shape between one type of map and another, especially

photo maps, would provide at least one logical test of how clearings

might be used by navigators. The argument is: if there is a

consistency of clearing shape across map type, then it would at

least be possible for a navigator to use clearing shape for navigation.

Conversely, if clearing shapes were inconsistent over map types,

then it would not be possible for a navigator to rely on clearing

shapes on all maps. This comparative analysis was a qualitative

rather than quantitative one. Results were as follows: First it

was observed that for the three maps used to obtain the night

navigation data, the general shape of a large clearing is consistent

from one map to another; but, the shape of smaller clearings and

small indentations of larger clearings are not consistent across map

types. Second, map contour lines tend to define a clearing shape

to the eye even when the clearing does not follow the contour -

this factor may lead to navigator error when extracting clearing

shape from a map. Finally, it was observed that the specified

41
routes often pass over vegetation between clearings or at the edge

of clearings. This seems to occur where a course heading change

is made. Typically the route specifies that the aircraft pass one

or more clearings prior to making a heading turn, and when the

turn is made, the flight path is at a clearing edge or over vegetation

between clearings.

This tendency suggests a hypothesis regarding clearing

isage. It might be (particularly in the absence of a clearing with

a uniquely reliable shape) that clearings are used as dividers

separating possible paths which extend from an existing route

direction to an alternate route direction. With this hypothesis as

to clearing usage, a route might be laid out to pass by one or

more clearings and then turn to pass between or along the edge

of a clearing. The route designer's rationale is that the navigator

would be able to count clearings along the route prior to the turn

and that counting or recognition of clearings would aid in recognizing

the correct location of the turn. Analysis E, "Multiple Clearing

Patterns Along a Route," was designed to test that hypothesis.

The results of Analysis E do not support the assertion

that T 3 /T6* and T 3

2 /T 6 * , taken as individual factors, are

related to PS; but, with the multivariate regression significant (p < .05)

prediction of PS was obtained. The factor T 3 /T 6 was positively

correlated with performance while T 3

2 /T 6 was negatively correlated

with performance. This result means that PS is high ,where deep

indentations of vegetative terrain separate clearings - this occurs

where an indentation, if large enough to exist 300 meters from the

route, extends to 600 meters from the route. Conversly, PS is

low where indentations exist at 300 meters but not at 600

meters from the route. Thus it seems that deep separations

between clearings presumably aid the navigator in distinguishing

adjacent clearings and result in improved performance. This

tends to s:upport the hypothesis that navigators at least use the

number of clearings along a route to successfully identify a route

turn. The result is consistent with the comparison of clearing

shapes represented in different types of map§: large (at least

600 meter) indentations of vegetative terrain and clearings are

reliable indicators of clearing shape. Conversely, the hypothesis

states that small indentations are not reliable indicators of

clearing shape.

*T 3 /T 6 is a measure of the extent of an indentation (by vegetation) '

of a clearing and T3

2 /T 6 is a measure of the number of such

indentations.

42
The second factor (T 3

2 /T 6 ) used in the multivariate

regression reflects the number of vegetation indentations both

small and large. Its negative correlation with PS suggests that

many clearings along a route prior to a turn result in low

navigation performance.

EFFECT OF GENERAL TERRAIN FEATURES ON ROUTE SEGMENT PS

(ANALYSIS F)

A search of all the 45 terrain features used as general

terrain classifiers resulted in only four feature classes with

significant prediction of total route performance. This prediction

uses the proportion of intervals (PI) along the route associated

with the feature type as the independent variable in a regression

analysis. The dependent variable was route segment PS. The

proportion of a route containing the terrain feature of interest is

a variable describing the general terrain along a route or route

segment. This terrain classification is applicable to many

successive intervals - not just one interval.

"One valley" was the best single feature predictor

accounting for 93.7 percent of the variance. Furthermore, " 1

valley" was positively correlated with the route PS implying that

the higher the proportion of "l valley" along the route, the higher

the expected navigation success.

The other three feature categories "1 to 3 fingers,"

"12 or more valleys," and Il to 3 draws"t which provided significant

route PS prediction were negatively correlated with PS. This

implies that the less frequent the type of terrain along a route

is present, the higher the route PS. Previous results (Analysis

B) confirm that the existence of multiple valleys at an interval

produces reduced PS - perhaps because of the multiple opportunities

for error.

The same previous analysis shows that "4 or more fingers"

and "14 or more draws"t provide improved PS at intervals. Thus,

it is not inconsistent to observe the negative correlation of "1 to

3 fingers" and "1 to 3 draws" with route PS.

When all combinations (taken two at a time) of the four

terrain features found to be significant in the univariate analysis

were tested, only two combinations were found to be significant -

"1 to 3 fingers, 2 or more ponds" and "1 valley, 1 or more

plateaus." Both combinations accounted for a high percentage of

43
route PS variance. However, it must be noted that route (PS)

scores were used and thus only four data points were available.

The test of the relationship between the variable PI

(1 valley) and PS for one-half and -one-quarter route segments

shows that a strong relationship exists for one-half route segments.

PS over one-quarter route segments have a weak relationship to

PI (1 valley). This suggests that there may exist a lower bound

to the length of route segments that can be classified according

to general terrain type. Analysis G was an investigation of the

existence of such a bound and is discussed in the following

paragraph.

No significant relationship was found between ambient

light level and performance.

SIGNIFICANCE OF THE LENGTH RESTRICTION OF "1 VALLEY"

(ANALYSIS G)

Results of Analysis G show that the 2,500 meter lower

limit on the definition of "1 valley" does not restrict the performance

prediction capability of the "1 valley" variable. Thus, the factor

PI (1 valley) can be used to predict performance of routes and

route segments greater than 2,500 meters in length.

SIGNIFICANCE OF "1 VALLEY" CONSTITUENT TERRAIN

FEATURES ON PS (ANALYSIS H)

The term "1 valley" has been defined in a very

specific way and it is well to review that definition here.

Further, the effect of an route PS of each component of

that definition was investigated. That study revealed critical

aspects of the definition. A terrain interval is classified

as a "1 valley" interval if it satisfies all of the following

conditions:

44
1 Route follows a valley over length of

the interval. (VT)

2. No depression (other than the depression

the route follows) rises less than 100

feet (or to the elevation of high features)

over 1,000 meters to either side of the

interval. (DT)

3. The depression which the route follows

is less than 2,000 meters in width

measured across the bounding elevation

on each side of the interval. (NT)

4. Route rises less than 60 feet per 1,000

meters over the length of interval. (ET)

5. Route interval is greater than 2,500

meters in length.

Note that the definition of "I valley" is similar to that

of a salient valley except that "I valley" has the additional

2,500 meter minimum length restriction.

Results of the correlation analysis as shown in

Table 15 reveal that the factors VT, NF, ET are highly

correlated and therefore cannot be considered as independent

factors. This relationship is a result of the route design

which specified routes where the three factors coincide

frequently. Because of this relationship, the associated

terrain could be referred to as "route in valley," "a wide

valley" or "a flat valley." Also it is not possible to logically

associate navigation performance (Ps) with just one of these

terrain characteristics. Note also that these conditions are

negatively correlated with PS.

45
Tables 16, 17, and 18 show that all statistically significant

condition combinations involving NT(NF) are always positively (negatively)

correlated with route segment PS. This correlation property is true

for all route segment lengths (i.e., quarter, half, full routes) and

is true when NT (and NF) is combined with the other variables (H

and D) ). (The same correlation property holds true for the V factor

which was previously shown to be correlated with the N factor.)

This consistent correlation property strongly suggests that the relationship between P 5 and the overall N condition (and therefore V

and E) dominates that of the D and H factors.

Considering now specific condition combinations and referring

to Table 16, combinations NTHT, NFDF, NTDF explain 41 percent,

32 percent and 30 percent, respectively, of the variance of the

interval Ps. The first combination (NT HT) correlation coefficient

(.645) is an increase over the individual correlation coefficients of

NT (.504) and HT (.225). This evidence tends to support the

assertion that the combination NTHT is a better predictor of quarter

route PS than NT or HT alone.

The assertion is reinforced by the results of the half route

analysis shown in Table 17. In addition, the combination NFHF

accounts for 80 percent of the variance and NFDF accounts for 72

percent of the variance. This suggests that a high proportion of

navigation errors occur at intervals classified as either NFHF or

NFDF, i.e., where the valley width exceeds 2,000 meters and either route

heading changes exist or there are other depressions. Check of

the possible assertion that the triple combination NFHFDF may

provide an even better PS prediction (i.e., explain more variance)

reveals a lower variance explained (61 percent) as shown in Table 17.

Finally, the results of fUll route regressions also show

that NFHF and NFDF combinations explain 95 percent and 92 percent,

respectively, of the full route PS variance. Apparently, it can be

concluded since these combinations7 are negatively correlated with

route PS, that the combination of conditions represented by NFHF

and NFDF should be avoided in route design. Also, it is seen that

the triple combinations NTH TO T and N TH F D F explain less variance

than do the double combinations referred to above. This suggests

that the specific combinations of conditions NFHF and NFDF define

terrain conditions to be avoided and that terrain represented by the

dual combinations provides a more serious problem than does

terrain represented by the individual conditions NF, HF' and DF. (

46
NFHF and NFDF specify conditions to be avoided in

route design; but, according to the results shown in Table 18

the combination VFHFDT, which is positively correlated with

route PS, explains 99.8 percent of the route PS variance. This

result suggests that where route heading changes occur (i.e.,

HF is true), the route should not be in a valley and there should

be only one local terrain depression.

EFFECT OF ROUTE HEADING CHANGE ON ROUTE SEGMENT PS

(ANALYSIS H, J, K)

The relationship between route heading changes and route

segment PS was investigated in three ways. Analysis J, which

was a test of the effect of a route heading change considered abovewithout regard to local terrain conditions, revealed that there is no

significant effect of heading change when it is considered as an

isolated variable. Results of Analysis H clearly reveal that heading

changes that occur together with wide valleys (recall that wide

valley occurred frequently with "routes in the valley" and a "valley

floor with a gradual slope") lead to frequent navigation errors.

Analysis H also revealed that route heading changes occurring

where there is only one valley (i.e., no other depressions), but

where the route is not in the valley, are frequently associated with

navigation success. Finally, the results of Analysis K which are

summarized in Table 20 again show that combining heading change

together with certain terrain features does permit explanation of

additional route segment variance over that of the terrain feature

alone.

Based on these analyses it must be concluded that there

is an interaction of route heading and terrain features that should

be considered in route design. In contrast, however, it is seen

that the terrain conditions of "1 valley" permit a prediction of

route segment PS that explains considerable variance, but that

the combination of "1 valley" and heading does not explain much

additional variance. Therefore, the condition "1 valley" can be

used to predict route segment PS without regard to route heading

changes.

EFFECT OF TERRAIN CLASSIFIED BY GENERAL TERRAIN

TYPE AND BY LOCAL TERRAIN FEATURES (ANALYSIS L)

Rationale for selecting three PI (1 valley) factor classes

was to establish three easily visualized PI (1 valley) factor conditions.

PI (1 valley) Class A represents general terrain type not frequently

47
associated with one valley. PI (1 valley) Class B represents a

mixed general terrain type where the route enters and leaves a

single valley. PI (1 valley) Class C represents route segments

almost totally in a single valley. It is believed that this classification system permits an individual to easily translate from the more

abstract "PI (I valley) factor" classes to map areas associated

with the general terrain type.

However, the numerical values for interval PS for the

three classes suggest that perhaps two classes would be more

appropriate. PS for Class A are different (lower) than those of

Classes B and C. Perhaps the reason for this empirical merging

of Classes B and C is that in Analysis E, one-quarter route

segments were used and classified according to the PI (1 valley)

factor. Within each of these categories, significant local terrain

features were determined. But the regression analysis (Analysis D)

showed that prediction of one-quarter route PS was not as significant

as prediction of PS for one-half and full routes. Perhaps if onehalf routes had been used for Analysis E, three performance

groupings would have emerged. There does not seem to be an

advantage of three groups over two, but from the results of the

regression analysis (Analysis D), three groups were expected.

In the subsequent analysis (route and student scoring) three PI

(1 valley) factor classifications are used to preserve notation.

Since analysis has shown the importance to interval PS

of general terrain classified as "proportion of intervals associated

with "1 valley," methods of identifying "1 valley" terrain on a

map should be reviewed. "One valley" is taken to mean terrain

where the valley floor is of a constant elevation (less than 60

feet elevation change per 1,000 meters) and both elevated sides

of the valley are visible (within 1,000 meters) from the flight

path along the valley floor. Terrain that otherwise would be

classified as a valley, but where the elevation is increasing or

decreasing, are classified as "draws." Also, terrain that is

broad and flat so that the distance from one side elevation across

the floor to the other side elevation is greater than 2,000 meters,

is not classified as "1 valley." Further, a valley was defined as

having a length exceeding 2,500 meters. Thus, only terrain consisting of a narrow, single valley at least 2,500 meters long where

the valley floor is of constant elevation is classified as "1 valley."

48
It is interesting to note that exceptiqnal terrain features

associated with PI (I valley) factor Class A are mostly unfavorable

features. Thus, referring to Table 21 which applies to all maps,

if there are no bridges, no draws, or no fingers at an interval,

performance is expected to be degraded as evidenced by the low

PS indicated. The favorable effect of "4 or more fingers" and

the unfavorable effect of "no draws" and "no fingers" is not

unexpected due to results of previous analysis. However, observing

that the finger and draw factors are not exceptional factors for

Class E and Class C suggests that something about the general

terrain characterized by the Class A category makes fingers and

draws important to navigation performance. Recalling the discussion

in the previous paragraph as to what terrain was classified as

"1 valley," the results suggest that in Class A terrain, especially

in broad depressions (which in this study contained multiple valleys

and thus were. not classified as 1 valley), routes should be selected

to run close to (within 1,000 meters) one side elevation where

fingers and draws are visible and not along the center of the

depression.

Comparison of interval PS for all maps with the PS for

the NP-IC map in the general Class A terrain reveals that PS is

about the same when fingers and draws are present. But some

property of the NP-1C map permits good performance when fingers

and draws (presumably terrain detail) are absent.

The low interval PS for all maps (general terrain Class

A) when "1 or more buildings" are present, may result from a

disturbance of the navigator's dark adaptation when building lights

are present.

The terrain feature of importance to navigation success

in general terrain Class B is "4 or more hills." This result

applies to all maps both individually and considered together.

Apparently in Class B terrain of multiple hills can be relied on

for superior performance.

Water features of importance for both Class B and C

are "2 or more ponds" and "no streams." The importance of

these factors was discussed in a previous paragraph.

For the Class C terrain and "all maps" as well as the

individual map NP-1C, PS is sensitive to ponds and dams. For

all maps,"|2 or more ponds" are important to interval PS while for

the NP-1C map "1 pond" is associated with exceptional performance.

49

IL Li
ROUTE AND SUBJECT SCORING

METHOD

Analysis of the Independence of Interval Navigation Errors.

How can the route PS be calculated from the interval PS? If the

probability of a navigation error foreach route interval is independent

of navigation errors for other intervals, then a simple product rule

(product of the PS's for each interval along the route) should yield

the correct probability of success for the route. In order to test

this calculation rule, which is an application of the binomial theorem,

a theoretical prediction was made of the probability of success for

a route (probability of exactly no error) along with a prediction of

the probability of exactly one error, exactly two errors, etc. The

binomial theorem was used for these predictions and the PS per

interval for the theorem was calculated for all subjects on all

routes. This theoretical distribution of the number of navigation

errors per route was compared with the actual distribution of the

number of errors per route.

A chi-square test was used to compare the two distributions.

If the two distributions cannot be distinguished, as would be

evidenced by a lack of significance of the chi-square test, then

the product rule can be accepted as a reasonable way to calculate

route Ps.

Calculation of Route PS. The product rule was used to

calculate PS for each of the four routes. Route PS was calculated

for each of two map categories "all maps" and the NP-1C map.

The procedure for assigning PS to intervals along a route and

calculating PS for that route is as follows:

1. Divide the route into 500 meter intervals.

2. Group intervals into segments according to

the PI (1 valley) (valley terrain classification

system) described previously. Classify each

segment as PI (1 valley) factor Class A, B or C.

3. Select Table 21 and 22 according to map type

(If map type NP-IC is to be employed, use

data shown in Table 22. If any of the three

maps is to be employed, use the data shown

in Table 21.

50
4. For each interval, identify the terrain features

present and determine if one or more terrain

types is identified as extraordinary in the

appropriate table (selected in Step 3 above).

If a terrain feature is identified as extraordinary,

use the indicated PS value given in the table

for the interval. If no extraordinary terrain

feature is present, use the PS value indicated

by the "other terrain types." If more than one

terrain feature in an interval is identified as

extraordinary, the larger (or largest) indicated

PS is used.

5. After PS values have been assigned to each

interval along a route, the product of the

interval PS values is calculated as the route

Ps value.

Calculation of Subject Scores. The purpose of the

subject scoring method (SSM) is to provide a weighted score for

successful navigation of an interval depending on the terrain features

in that interval. Scoring is to be based on the interval P5 achieved

at each type of terrain where PS is determined from the experiment

flight data. This provides a general subject scoring method not

limited to scoring of performance along routes flown in the

experimental flight program.

As an aid in the description of the subject scoring method

(SSM), consider all intervals where the probability of success is

equal to Psi. Suppose that for each navigation success along those

intervals a subject is given an interval score of

1

P~i (1) Psi

Suppose further that after N i attempts, a subject has NSi successes

at those intervals. The summation of scores for those successes

will equal

NSt

SUM = (2)

But the probability of success for that subject at those intervals

can be defined as

51
NSi

P1S - N. or NSi N P'Si (3)

Substitution yields

N. P,

SUM Pi Si (4)

If the sum is divided by the total number of attempts (Nt) then:

SUM PSi (5)

N N. P F' S

If the subject average performance (P'Si) is equal to the expected

performance (Psi), the value of the normalized sum (SUM/Ni)

equals 1. If he performs better than expected, his score will be

greater than 1, and less than 1 if he does not perform well.

Note that the expected PSi is the average PSi over the subjects

performing in the experimental flight program.

Of course, the subject scoring method of interest deals

with performance in each interval along a route not just in intervals

where the PS equals PSi" If PS3 is now taken to represent the

expected probability of success in interval j, then the subject score

for a route trial is

N k.

SCORE 1 % (6) M j=1 PSj

where

P S is the expected probability of success in interval j;

K. = 1 if the student is successful in interval j,

otherwise K. = 0;

N is the number of intervals along the route;

M is the number of intervals attempted.

52
Note, the expected value of SCORE is obtained if Kj is replaced

by the expected probability of success in each interval (Psj), and

therefore, the expected value of SCORE is 1. As a result, a

subject performing better than average will achieve a score greater

than 1, and less than 1 if he does not pet-form well.

There is an easier way to calculate scores for a subject.

Since subjects experience success in many more intervals than

they have errors, it is easier to first compute the score possible

if all intervals were completed successfully and then to correct

that sum for the navigation errors. The procedure is as follows:

a. Compute the sum of 1/Psi over all route

intervals, i.e.,

N

1

SUM = (7) j=1 PS

b. If a navigation error occurs, say in interval

k, substract the score value for that interval, i.e.,

1

NEW SUM = OLD SUM - p () ~Sk

c. If an interval is missed, say in interval k,

subtract 1 from the total interval count and

subtract the score values for that interval

from the sum, i.e.,

NEW N = OLD N - 1 (9)

1

NEW SUM = OLD SUM - (10) Sk

d. When all intervals that were missed or that involved

navigation errors are processed, the subject's

score is computed as

NEW SUM SCORE = NEW N (11)

53
RESULTS

Independence of Interval Navigation Errors. Results of

the analysis of the product method of predicting route PS from

component interval Ps(s) is given in Table 25. Since the chisquare test is not significant, there is no reason to believe that

the distribution of errors developed by the product rule (binomial

theorem) (i.e., probability of exactly no errors, exactly one error,

exactly two errors) is different from the distribution observed in

the experiment results. This implies that multiple errors on a

given route trial can be considered as independent errors. It

also implies that the product rule (product of interval PS's) can

be used to predict the route PS.

Table 25

ANALYSIS OF DISTRIBUTION OF NUMBER OF

ERRORS PER ROUTE

Experiment Data Binomial Theorem

Number of

Experiment

Trials With

Specified Expected

Number of Number of Number of

Errors Errors Probability Probability Trials

0 11 .141 .151 11.778

1 21 .269 .294 22.930

2 24 .307 .277 21 .606

3 14 .179 .168 13.104

4 7 .089 .076 5.928

5 1 .012 .025 1.950

Total 78

x 2 (5) 1.193, p ( .05

54
Table 26 shows the effect of interval Ps'S values on

the route PS value when the product rule is used. As an example,

a PS is calculated for a route containing 30 intervals with each

interval having the same PS. The corresponding value of interval

PS is shown along with the change in route PS for a .01 change

in interval PS (computed as the derivative of route PS with respect

to PS x .01). It is shown that the improvement in route PS

increases with increased interval PS. This result illustrates the

importance of interval PS in achieving high route PS, and that

the relationship is not one of "diminishing returns," but instead,

it is one of "increasing returns."

Calculation of Route Probability of Success. Route

probability of success values calculated for all maps and for map

type NP-1C are shown in Table 27. Route "difficulty" as measured

by probability of navigation without error is different for the two

map categories. This implies a differential improvement in

performance at certain types of terrain with the NP-1C map. The

superior performance of subjects using the NP-1C map is demonstrated by the results.

Table 27

ROUTE PROBABILITY OF SUCCESS

Route Number PS

All Maps

1 .097

2 .111

3 .166

4 .176

Map Type NP-1C

1 .468

2 .224

3 .401

4 .493

55
Table 26

EFFECT OF AVERAGE INTERVAL PS ON ROUTE PS

Value of Derivative

Average Interval (d PSR x .01

S SR dPS

.89 .030 .101

.90 .042 .014

.91 .059 .019

.92 .082 .026

.93 .113 .036

.94 .156 .049

.95 .214 .067

.96 .293 .091

.97 .401 .124

.98 .545 .166

.99 .739 .224

.995 .860 .259

PS is the probability of completing interval without navigational i

error.

PSR is the probability of completing route without navigational

error assuming 30 intervals on the route, i.e., P = P 30

SR S

56
Subject Scores. Subject scores for' each route are listed

in Table 28. Subjects are grouped according to map type. Average

scores for each subject and subject rankings are given in Table 29.

Subjects using the NP-1C maps tend to score high -

presumably because of the superiority of the NP-IC map. Score

weightings of each interval were based on performances of all

subjects and on all routes where all three map types were used.

For that reason and the superiority of map NP-1C, subjects using

the NP-1C map ranked higher even though they were not necessarily

the superior subjects.

DISCUSSION OF THE SUBJECT SCORING SYSTEM

Analysis of the Independence of Subject Errors. Can

interval PS be combined to predict total route PS? Evidence

supporting prediction of route PS as the product of the component

interval PS(s) is presented in Table 25. Experiment data for all

routes are summarized in the lefthand data column. The total of

78 experiment trials resulted in 11 route trials without error, 21

route trials with one error, etc. Probabilities of exactly no errors

on a route, exactly one error on a route, etc., calculated from

experiment data, are also listed in the table. The corresponding

theoretical probabilities and expected number of trials, developed

according to the binomial theorem, are also listed. A chi-square

test supports the conclusion that the two distributions are not

different. Thus it is concluded that prediction of route PS from

interval PS is feasible using the product rule (the binomial

theorem). Also, the expected number of errors per route is

predictable using the product rule. It should be emphasized,

however, that this conclusion is based on a study of aggregated

data for all subjects and all routes. While there is no evidence

to the contrary, it is not known if multiple errors by an individual

subject along a route can be considered as independent errors.

The effect of average interval PS on route PS assuming

a 30 interval route is shown in Table 26. The importance of

increasing the average interval PS by .01 on route PS is listed

in the third column. It is seen that payoff for improved interval

performance increases the higher the interval PS. It is also

seen that what might seem to be a small difference in PS at an

interval, e.g., A = .02 (.94 > .96), provides a substantial

difference in route PS, e.g., A = .137 (.156 > .293).

57
Table 28

SUBJECT SCORES

Subject Route 1 Route 2 Route 3 Route 4

5 .935 .908 1.030 1.023

6 - .825 1.030 1.053*

7 1.007 .976 .990 .990

8 1.010 .971 1.030 1.020

17 1.038 1.001 1.028 1.022

18 1.046 1.002 .948 .987

19 .858 .964 1.023 .982

20 .892 .984 .935 .989

SUBJECTS USING MAP TYPE AMD-1B

9 .985 1.03 .908 .990

10 .808 1.03 .994 .911

11 .890 - .985 1.053*

12 .868 .968 .826 -

21 1.046 .991 1.030 1.020

22 .965 1.0 .874 .986

23 .971 .943 1.028 .991

SUBJECTS USING MAP TYPE AMD-3A

13 1.005 .994 1.009 1.022

14 - .998 1.028 .988

24 1.007 1.060* .987 -

25 1.084* .964 1.065* -

26 1.084* 1.060* 1.065* 1.020

27 1.084* 1.060* 1.065* 1.022

SUBJECTS USING MAP TYPE NP-IC

Route means A = .978 1, = . 86 u = .994 # = 1.003

Standard deviation of = .078 • = .049 = .057 u = .024

route means

*No errors on flight.

Mean of subject scores - = .990

Mean of route mean scores - = .990

Standard deviation of subject scores - = .057

Standard deviation of route mean scores - e = .009

58
Table 29

SUBJECT AVERAGE SCORES AND RANKING

Number of Average

Subject Flights Score Rank

5 4 .974 15

6 3 .969 16

7 4 .991 11

8 4 1.008 7.5

17 4 1.022 4.5

18 4 .996 10

19 4 .957 17

20 4 .950 19

SUBJECTS USING MAP TYPE AMD-1B

9 4 .978 13

10 4 .936 20

11 3 .978 14

12 3 .887 !1

21 4 1.022 4.5

22 4 .956 18

23 4 .983 12

SUBJECTS USING MAP TYPE AMD-3A

13 4 1.008 7.5

14 3 1.005 9

24 3 1.018 6

5 3 1.038 3

26 4 1.057 2

27 4 1.058 1

SUBJECTS USING MAP TYPE NP-IC

59
Calculation of Route PS. The value of the route PS

is obtained by the product of the route interval PS's which, in

turn, are fLnctions of terrain type. Determining interval PS

requires classification of terrain type in two steps. First, the

terrain is classified according to the PI (1 valley). This classification is accomplished by dividing the route into segments

approximately 3,000 meters in length. The objective is to classify

the general terrain over a segment approximately six intervals

long (assuming 500 meter intervals). Classification is accomplished

by observing the proportion of intervals in 1 valley as follows:

Proportion of Intervals

Class in 1 Valley

A .00 - .33

B .34 - .66

C .67 - 1.00

When the segment is classified, the PS of each 500 meter interval

in that segment is obtained from Table 30. If, for instance, the segment

is classified as Class A, the PS for an interval is .976 unless

one or more of the "favorable" or "unfavorable" features, termed

special conditions, are in that interval. The existence of a special

condition requires selection of a new PS for the interval according

to the values given in the table. And if more than one of the

special conditions exist at the interval, then the one with the

largest PS is used. In an example where PI is classified as

Class A and an interval contains two fingers, a bridge and no

buildings, the PS would equal .886. Since the interval has two

fingers, the first and last conditions are not satisfied. Likewise

the second and third conditions are not satisfied leaving only

condition four "no draw." If instead there was no bridge, the

PS would be changed to .899. Table 30 provides the PS for

500 meter intervals and also identifies the cues found to critically

affect PS for each of the three terrain classes.

Instead of computing route PS by first determining the

values for each component interval and then taking the product

of interval PS's, a simplier direct method is available. Tables

31 and 32 provide the route PS for day (NOE) and night (contour)

flights respectively. In each table the ordinate is the length of

60
Table 30

PS FOR DOUBLY CLASSIFIED ROUTE INTERVALS

ALL MAPS

Features With p

S ignificantly t-test Favorable Unfavorable

Different PS df-- 20 Feature Feature

4 or more fingers .01 .980*

No bridge .02 .899

1 or more buildings .05 .894

No draw .01 .886

No fingers .05 .878

PS for all other features is .976 (18.7% of Class A intervals)

PI (1 valley) - Class A

No stream .001 1.000

1 or more railroads .001 1.000

2 or more ponds .001 .983

4 or more hills .001 .979

1 or more bridges .001 .969

1 or more dams .01 .962

PS for all other features is .883 (14.6% of Class B intervals)

PI (1 valley) - Class P,

2 or more ponds .001 .988

1 or more dams .001 .983

PS for all other features SI is .931 (83% of Class C intervalt)

PI (1 valley) - Class C

*Only five intervals

Note: PS over all intervals without regard to terrain feature

is .936.

61
(V3

0 0

Dli

OD- 0

co- '- 0)

0 (D CD

U0 -f ' ' OD 

Z 0

c

CD)

00

C) U) 0 Y 0 z 0 0 0 0) is ) 0) 1

11 cr (m0 0) . 0 0

0 (0 ol CIDD' q 0m 0

Z C (0) 0 OD co Ni 0) is4)

LL -iL 0) 0() (D (D OD rN D 0 -- 0- -O 0 D C0

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r- 0- 0) (Y) C~ CD N 0)0 

00) D 0~ 0 CD) 0 to 0) C) CD 0

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62
OD 0 0

0 0 0

%-,n co if) (D

>- C'i OD L

N-' 0 0 0

0 c 0 -'

0) 0

0 0O

0 n r-j 0 0

0) M DN o 0

0)

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to (V ) C') '- ' 0 0 0 0

AiC

0 4)* 0 I (D C) co I 0 C\D In 0

63
the total route and the abscissa is the length of the route in

1 valley. These tables provide numerical solutions to the

following equations.

N V N N

PS N.863 + .846 for day flight, and

N v N N '

P = .893 + .869 for night flight

where N is the length of the segment (in KM) in 1 valley and

NN is the length (in KM) not in 1 valley.

Subject Scoring Method. The subject scoring method

(SSM) normalizes interval scores according to historical performance data. This provides an expected score of 1.0 for any

route design and any route length - even for a route not previously

used.

A student performing as an "average" student will score

a value of 1 .0 on a route. Superior students or "average" students

using superior equipment (such as the NP-IC map) will socre

higher than 1 .0. Of course, if the superior equipment becomes

standard then the reference interval PS values would be updated

so that the expected score would again be 1.0.

The ability of the scoring system to provide a "standard"

student evaluation score (i.e., an expected score of 1 .0) is evidenced

by the mean scores for each route. As shown in Table 28, the

mean of the route mean scores is .990 and the standard deviation

of the route mean scores is .009. These data support the contention that the student scoring system automatically compensates

for differences in route difficulty. The standard deviation for

the routes of .009 accounts for both sampling errors and student

scoring errors (i.e., error in accounting for differences in route

difficulty). That standard deviation is less than the standard

deviation of subject scores (.0571). As a result, it appears

reasonable to establish a student evaluation system where average

students' scores would be .990 + .05 independent of route difficulty.

64
A scoring method, such as the SSM, that provides a higher

incremental score for success in intervals with the type of terrain

frequently associated with navigation errors would seem to be a useful

method. The inverse of interval PS, i.e., 1/PS, indicates the degree

of navigation difficulty of an interval which should be taken into account

for both route planning and student scoring.

The SSM worked reasonably well in scoring subjects in the

experimental program and should be evaluated by instructor pilots.

Tables 33 and 34 are scoring aids that might be useful for the instructor.

To use the tables, the route the student attempts to navigate must be

divided into 500 meter intervals and each interval classified as

described in the section "calculation of route PS." According to that

classification, each interval is associated with one 1 valley class and

one terrain type (of more than one terrain condition is true at an

interval, the condition with the lowest score value is assigned to the

interval). The associated score shown in the tables, which equals

I/PS, is the score to be assigned to the student for successful

navigation of that interval. A "mark" can be placed in the table for

each interval navigated successfully. The route score is determined

by summarizing the scores for each interval navigated successfully

and dividing that sum by the total number of intervals. The value of

an average student will be .990 + .05. A superior student's score

will exceed .995.

It should be noted that the interval PS values given in the

tables for various map and terrain types are actually the PS per 500

meter interval. If larger intervals are used, such as 1 000 meter

intervals, the product rule can be used to calculate the new PS. For

example, if a 1 ,000 meter interval is used which includes two 500

meter intervals with the same terrain type, then new material

Ps = old interval PS 2 .

DAY NAVIGATION ANALYSIS

GENERAL METHOD

Data for the day navigation experiments were recorded in a way

similar to that of the night data. Reference routes and the actual subject

flight path were marked on maps. Ten different routes were flown with

a different set of subjects flying each route. Seven routes were flown

by 10 subjects each. Two routes were flown by nine subjects and one

route by four subjects. Two different types of maps were used. One

map, "PM-50," was used on seven routes. The other map "TM-20"

was used on three routes.

65
I.

L L

0 o

C U) U

8 4C L L

"H 0 0 cU

I Lo- 0O. "f3 0 01 .c Uo(n

LL 0- 4)3 (d

H +

00

0 0

zL. -. -0 L. ) T~ 0) U

009 0 - 0 C- L

M 2 C.D a. 0 

< 0

C'-

> (d 0

L L

o L m

0 0(D

0D-- C Cu 

L L) U)

"o C- 0(a)C.

a) L~ 0o N0r C-l 

L 0 0

1 0 + 0

0- C '

66>
LI

00

0 4-1

00

ti

a) U I 

o > L LO (1

o -0 4-U1

F- (- u)

z :

w

D U)

0

0

o8

67
Differences between the day data and the night data

included use of a map with 50 foot contour intervals (PM-50),

the use of different subjects on different routes, and the use

of routes of substantially different length. Also, the set of

45 terrain features previously used, (listed in Table 1) was

modified. Railroads, rivers, intermittent streams, power lines,

and bridges either did not occur at more than a few intervals

or were not coded on the maps. Rivers and intermittent streams

were considered to be included with streams on the map. Table

35 lists the terrain feature types with notation regarding the day

navigation data. Table 36 provides a tabulation of the differences

between the night and day navigation data.

The effect of the 50 foot contour intervals on map

PM-50 caused difficulty in identifying terrain features. It is

believed that some hills were not detected and an accurate count

of terrain fingers and draws was not obtained. Review of terrain

coding showed that some valleys may have been coded as draws.

Because of this probability, draw coding was reviewed and draws

were divided into two categories - major and minor draws.

Minor draws are draws that have a floor length of 250 to 1,000

meters while major draws are from 1,000 to 2,500 meters in

length.

Because the night navigation experiments used the same

set of subjects over all four routes, a repeated measure analysis

was used. This method employed differences in subject scores

for different routes, and different types of terrain. In order to

employ a similar methodology for the day navigation data, subjects

were paired according to overall performance on each route. For

example, the best performer on one route was paired with the best

performer on each of the other routes. Accordingly, 10 composite

"subjects" were identified on six routes of approximately the same

length. Table 37 documents the matched subjects identification.

Route numbers 2, 4, 10, 11, 12 and 14 were selected for detailed

analysis. Data from the seventh route, also performed by 10

subjects, were not used because that route was considerably

longer than the other routes.

Terrain features along the six routes were coded using

the same feature identification criteria employed for the night

data. However, these data were not entered into the computer

but were instead listed in charts for hand analysis. The simplified

hand analysis was selected because time did not permit coding of

all the terrain feature parameters coded with the night data. Much

68
Table 35

EXPANDED FEATURE CLASSES WIH

NOTATION FOR DAY DATA

Class Type of Number of

ID Terrain Features in

Number Feature Class Notation

1 Ridge None

2 Ridge 1 or more

3 Hill None

4 Hill 1, 2, or 3

5 Hill 4 or more

6 Finger None

7 Finger 1, 2, or 3

8 Finger 4 or more

9 Valley None

10 Valley 1 Only

11 Valley 2 or more

12 Draw None

13 Draw 1, 2, or 3

14 Draw 4 or more

15 Plateau None

16 Plateau I or more

17 Rivers None ) Did not

18 Rivers 1 only exist in

19 Rivers 2 Only any interval

20 Stream None

21 Stream 1 or 2

22 Stream 3 or more

23 Int. Stream None ) Not coded

24 Int. Stream 1 or 2 on map

25 Int. Stream 3 or more PM-50

69
Table 35 (Concluded)

EXPANDED FEATURE CLASSES WITH

NOTATION FOR DAY DATA

Class Type of Number of

ID Terrain Features in

Number Feature Class Notation

26 Pond None

27 Pond 1 Only

28 Pond 2 or more

29 Highway None

30 Highway 1 or more

31 Road None

32 Road 1 or 2

33 Road 3 or more

34 Railroad None Occurred in

35 Railroad 1 or more only one interval

36 Building None

37 Building 1 or more

38 Developed None

Area

39 Developed 1 or more

Area

40 Power line None Did not exist

41 Power line 1 or more in any interval

42 Dam None

43 Dam I or more

44 Bridge None Not coded

45 Bridge 1 or more on map

70
Table 36

DIFFERENCES BETWEEN DAY AND NIGHT NOE DATA

Item Night Navigation Data Day Navigation Data

1 One area of operation Three areas of operation

2 Four routes Ten routes

3 Three map types Two map types

4 All maps 20 feet contour One map with 50 feet

contour

5 More rugged terrain,

greater elevation difference

6 Matched subjects design Random selection of

of the experiment subjects

7 Terrain coded as a Single maps used for

composite of all three terrain coding

map types

8 No intermittent streams

and rivers coded

9 Greater number of Same features did not

different features - occur

railroads, power line,

bridge were included

10 Plateau - wide flat area Plateau - more like a

to side of wide valley ridge

11 Less ridges coded More ridges because of

50 feet contour lines on

map

71
Table 36 (Concluded)

DIFFERENCES BETWEEN DAY AND NIGHT NOE DATA

Item Night Navigation Data Day Navigation Data

12 More hills detected and Fewer hills detected and

coded coded

13 Average route length Shorter average route

16 Km length = 10.5 Km

15 Draws defined as Minor draw defined as

less than 2,500 250 to 1,000 meters

meters in length long. Major draw

defined as 1,000 to

2,500 meters long

16 Four routes coded Six routes coded

17 More developed areas,

more intervals 1

involving buildings

72
Table 37

MATCHED SUBJECTS - DAY NOE

Composite Route 3 Route 4 Route 10 Route 11 Route 12 Route 14

Subject

Number Subject on Routes

1 3 4 6 1 2 6

2 10 9 7 2 4 2

3 1 6 3 3 5 7

4 2 7 10 5 6 1

5 7 10 2 6 9 4

6 8 5 8 4 7 5

7 9 8 9 8 8 8

8 4 1 1 9 10 3

9 6 2 4 10 1 10

10 5 3 5 7 3 9

73
of the night parameter data that were coded were not used in

the night data analysis. Thus, a similar analysis could be

applied to the day data with a simplified coding of data. In

order to simplify the hand analysis, 1 ,000 meter intervals were

used instead of the 500 meter intervals used for the night data

analysis.

Analysis M: Calculation of the Probability of Success

ES in Intervals Classified by a Single Terrain

Feature Type.

METHOD. Intervals classified by the reduced number of

terrain feature types were searched in a manner similar to that

of night data Analysis A. This method uses test of the differences

in interval Ps as a function of the number of terrain features of

a given type. For example, interval PS for "no ponds" was

compared to the PS for "1 pond" and then to the PS for "2 or

more ponds." A Wilcoxon test was used to determine the significance

of differences in PS.

RESULTS. Tests of the significance of the number of

terrain features of a given type showed that changes in the number

of terrain features did not have a significant effect on interval PS.

Analysis N: Prediction of Route Pc_ as a Function

of the General Terrain Type Along the Route.

METHOD. This analysis corresponds to Analysis D as

applied to the night navigation data. The proportion of intervals

along a route associated with a given route is used as the

independent variable in a regression analysis. Composite PS for

a route is the dependent variable where route composite PS was

computed as the number of successfully completed route intervals

(without error) divided by the riJmber of intervals attempted. An

F test was used to determine the significance of the regression

function.

RESULTS. Results of the regression analysis showed

that the proportion of intervals associated with "I valley" or "i

major draw" (but not both) is significantly related to the route

composite PS, F (1 ,4) 46.41, p S .005. Table 36 lists the data

and regression results.

74
) L

0 L

4) 0

OD) 0 0 0

c d) .0)

CL U)

L)

M >

0 (f0

M 4)

00

U)

w

0 IO0 w 0

V,

E

Zcli 0 c') (

IL

75
Analysis 0: Effect of Route Geometry on Interval PS.

METHOD. Analysis 0 is an analysis of the effect of

route geometry on interval PS. PS for intervals involving route

heading changes greater than 50 and heading changes greater than

300 was compared to the PS for intervals involving no heading

changes. Data was completed for each of the six routes and a

Wilcoxon test was used to test the significance of interval PS

differences.

RESULTS. Results show that route geometry does

significantly affect interval PS. All heading changes greater

than 50 result in a reduced interval PS (.928 for straight intervals

versus .812 for intervals involving heading changes), (x 2 (1) =

20.5, p < .005). A similar result shows a reduced PS for

intervals involving large heading changes (greater than 300)

(.928 for straight intervals versus .815 for intervals involving

large heading changes), (X 2 (1) = 13.9, p < .005).

Discussion of Day Navigation Data. The lack of a

significant effect of the number of terrain features in intervals

on interval PS represents a difference in the result of the day

and night data analysis. It is not known if this result is due to

true effects of day navigation performance or reflects the difficulties

experienced in coding maps with 50 foot contour intervals.

Prediction of route composite PS as a function of general

terrain type, i.e., proportion of intervals involving one valley or

one major draw was significant and is consistent with the results

found in the night data. Apparently, the existence of a single

valley defines a fundamental type of terrain which is significant

to navigation performance.

The concept that route geometry affects performance

was demonstrated with both the day and night data. The reduction

in interval PS from .928 to .812 represents an important factor

in route PS and thus should be considered in route planning. The

sizes of the turn may be a significant factor but this factor was

not analyzed.

76
CONC LUS IONS

1. Of the 45 terrain factors tested, one terrain factor

was found to be the most significant single factor for predicting

route probability of success PS. This factor is P1 (1 valley) -

the proportion of the route in one valley. It is an important

factor for selecting routes, identifying cues along a route, and

evaluating the expected PS along a route for both day and night

navigation.

2. The factor PI (1 valley) is positively correlated

with PS; the condition VT (route is in a valley) is negatively

correlated with PS. This difference emphasizes that performance

is enhanced not by simply placing the route in a valley, but that

the valley must have certain properties.

a. The valley must be narrow (most important)

and there must not be other "valley like"

depressions in the immediate area.

b. Draws must be easily distinguished from

"other valley like" depressions. This can

be done by observing that draws have rapid

elevation to the horizon sufficiently close to

the flight path (say within 1,000 meters) to

permit the navigator to easily identify the

draw. Otherwise, the depression is "valley

like" - a condition where navigation errors

are likely.

3. The navigator, if working with a fixed route

plan, should pick cues as a function of terrain classified by

PI (1 valley) and seek redundant cues where detrimental terrain

influences exist.

4. The existence of multiple terrain features such as

two or more ponds, four or more fingers, four or more draws,

tends to improve interval PS significantly. Also, there is no

"'magic" about the specific number. For example, there is an

increasing PS associated with one finger, two fingers, ...

seven fingers and it appears that at the level of four or more

fingers, the results become significant at the statistical confidence

level selected.

77
5. Two combinations of terrain factors and route

geometry lead to navigation errors and explain a large percentage

of PS variance. These combinations are "a wide valley and a

route heading change" and "a wide valley and the existence of

other 'valley like' depressions in the area."

6. The I'-oute not in a valley, no heading changes,

and no other depressions in the area" is positively correlated

with PS and explains 99.8 percent of the PS variance over full

routes.

7. Ambient light level does not appear to affect PS.

8. The mere existence of one or more clearings does

not appear to affect PS" There is some evidence to support the

concept that performance improves where there are clearings

that are distinct from one another. This supports the hypothesis

that clearings are used as dividers separating possible route paths.

An alternative concept that clearings with a unique shape are used

as location identification cues could not be tested because "unique

shape" was not defined.

9. There is little question that map type NP-iC which

was used for the night navigation experiments supports superior

performance.

10. The 50 foot contour intervals on map type PM-50

caused considerable difficulty in identifying terrain features for

the study. It is suspected that the navigator would also have

difficulty in using that map for the same reason.

11. PS for a route or route segment can be obtained

by the product of PS for the component intervals.

RECOMMENDATIONS

The PS's calculated for various terrain types reveal

the expected navigation error rates for each type of terrain.

These PS values should be used in route planning (to select the

78
route with least probability of error), and cue selection (to look

for redundant cues in situations where errors are likely, i.e.,

PS is low). Further, the PS performance information should be

supplied to students in the classroom so they can become familiar

with navigation error rates associated with specific types of terrain.

A method of computing route on interval PS is given in the section

"Calculation of Route PS" (page 60).

A navigator planning his navigation strategy should select

his terrain cues based on the terrain classification according to

P1 (1 valley) because the important local terrain features are

different depending on the type of terrain as classified by the

PI (1 valley) factor. For example, if a route segment has a low

value of PI (1 valley), the existence of "4 or more fingers" can

significantly improve route segment PS; and further, the existence

of one or more buildings or the absence of any fingers and draws

can significantly decrease route segment PS. In contrast, consider

a route segment where P1 (1 valley) is high. In that case, the

existence of multiple fingers or buildings do not seem to be

important to segment PS; but, the existence of two or more ponds

can significantly improve route segment PS. Thus the navigator,

if working with a fixed route plan, should pick cues as a function

of terrain classified by PI (1 valley) and seek redundant cues

where detrimental terrain influences exist.

The normalized student scoring method (SSM) appears

to be a useful scoring concept which should be presented to and

evaluated by IP's in field trials. Evaluation of the method can

be accomplished from IP's comments. It may be desirable to

expand the scoring rule to account for the size of each error.

A method of computing student scores is given in the section

"Subject Scoring Method" (page 64 ).

Differential performance as a function of terrain type

was obtained with different map types. This suggests that each

map type may be beneficial in different ways. For example, the

NP-IC map supported best overall performance; but, with that

map, the existence of one or more buildings tends to reduce the

PS o Presumably, lights from the buildings, at night, affect the

navigator's dark adaptation. The question is: did the same

effect appear with the other map types? Why didn't the building

appear as a statistically significant factor for the other types of

maps? Studies of selective differences among the maps may

provide specifications for a composite map that is more useful than

the component maps used in the experiments.

79
The 50 foot contour intervals on map type PM-50 caused

considerable difficulty in identifying terrain features such as hills,

ridges and fingers for the study. It is suspected that the navigator

would also have difficulty in using that map for the same reason.

In contrast, maps with 20 foot contour intervals permitted identification of the required terrain features.

The use of logical (Boolean) combinations of terrain and

route geometry provided a sensitive measure of different factor

combinations without requiring many replications of experiment

trials (only four routes were used in the night experiment). The

method appears to have potential in empirical investigations.

Finally, correlation analysis among terrain and route

geometry conditions showed that several conditions were highly

correlated. This high correlation is due to the route design and

prevented investigation of the effect of the individual conditions

on route PS. While it is recognized that it is difficult to identify

all important factors prior to conducting an experiment, it is

worthwhile to evaluate correlations among experiment conditions

prior to conducting the experiment to insure that important

conditions are not highly correlated by the experiment design.

80
APPENDIX A

METHODS FOR REPRESENTING ELEVATION AND TERRAIN

FEATURES ALONG AN NOE ROUTE

INTRODUCTION

A first step in the analysis of NOE navigation error

patterns and the development of a NOE navigation performance

measure is the coding of terrain cues along NOE routes. Features

coded are those suspected of being used as navigation cues. The

terrain coding used is described in this appendix. The features

to be used are identified in the following section and where necessary,

definitions are provided. Parameters used to further categorize

each feature are also identified in this section. The system for

coding the location of a feature is presented in the section entitled

"Feature Location." The final section, "Route Coding," deals

with the organization of coded information, representing each NOE

route, for computer input.

FEATURES AND ASSOCIATED PARAMETERS

Information on the maps is organized in four categories:

elevation, terrain (vegetation, hydrography), and man-made.

Elevation features are defined in Table 1. The terrain features

(vegetation, hydrography, man-made) of interest are defined to be

consistent with those of the maps, however some differences exist.

Table 2 provides a listing of candidate terrain features with

definitions as necessary.

Associated with each feature are parameters which are

modifiers further describing feature characteristics. Table 3

specifies the parameters for each feature and Table 4 details the

unit of measurement for each parameter.

SIZE PARAMETERS

The size of elevation features is defined by the following

measures:

Ridge Distance along ridge top

Distance from top to base

81
Table Al

NAMES ELEVATION FEATURE

Hill A rounded elevation

Ridge A range of hills or mountains

Finger A lesser elevation extending

from a ridge

Saddle An elevation depression

connecting two higher points

Valley A long wide depression between

a range of hills or mountains

Draw A narrow depression between

fingers or ridges

Floor The low flat area of a valley

Plateau A large flat land area

elevated above adjacent land

on at least on side

82
* ,

Table A2

TERRAIN FEATURE NAMES

Name Map Symbol

Dense woods *

W\oods *

Scattered trees *

Orchard *

Marsh *

Scrub *

Cultivated *

Clearing *

River River is identified by a

double line on the map

Stream Stream is identified by a

single or broken line on

the map

Pond Any enclosed body of water

Highway Any road, two or more lanes,

medium duty or greater

Road Dirt or improved light duty road

Railroad *

Building(s) Any type of building

Developed area Ten or more buildings in a group

Towers Any man-made development

not otherwise specified here

(e.g., antennas, silo, light

or microwave tower)

Bridge Any bridge

Dam Any dam

Power transmission lines *

water tanks

* Definition consistent with a composite of symbols defined on map

types "Air Movement Data Experimental Prototype #B," "Experimental

Air Movement Data Red-Light Night-Use Prototype #3A," and

Experimental Night Prototype #1C."

83
Table A3

FEATURE PARAMETERS CODED

Elevation and Parameters Coded

Terrain

Features 1 2 3 4 5 6 7 8 9 10

Ridge x x x x x x

Hill x x x x x

Finger x x x x x

Saddle x x x x x

Valley x x x x

Draw x x x

Floor x x x

Plateau x x x

Dense woods x x x x

Woods x x x x

Scattered trees x x x x

Orchard x x x

Marsh x x x

Scrub x x x

Cultivated x x x

Clearing x x x

River x

Stream x

Pond x x x

Highway x x

Road x x

Railroad x

Buildings x

Developed towers x x x

Bridge x

Dams X

Power lines x

Water tanks x x

(1) Size (6) Type of Slope

(2) Shape (7) Amount of Slope

(3) Maximum elevation (8) Orientation

(4) Elevation (9) Type of Woods

(5) Minimum elevation (10) Light Beacon

84
Table A4

UNITS OF MEASUREMENT FOR FEATURE PARAMETERS

Size Meters or square meters

Shape Name, ratio or percent

of dimensions

Elevations Feet from sea level

Slopes Name types of slope and

average amount in degrees

Orientation Categories

1. N-S

2. NNE - SSW

3. ENE - NSW

4. E-W

5. ESE - WNW

6. SSE - NNW

Type of Woods Deciduous or coniferous

Light Beacon Yes or No

65
Hill Distance from base to top

Distance from base to base

Finger Distance from base to top

Distance between top of draw on

each side

Draw Distance from tops of fingers

on each side

Distance from base to top

Valley Distance measured at right angle

to route direction between two

highest points

Distance in line-of-sight along length

Saddle Distance from low point to high point

Distance from low point to other

high points

Floor' Length and width of flat area

Plateau Length and width of flat area

The size of vegetation and ponds is represented by the areas

portrayed on the maps.

SHAPE PARAMETER

Elevation Features

Ridge The number of high and low points

within 1,000 meters of a reference

point (see section "Feature Location"

for discussion of points along the

route).

Another shape representation of a

ridge is the ratio of length of width

Hill Ratio length to width

Finger Ratio of length to width

86
Shape of finger tip requires classification but a recommended method has

not been determined at this time.

Saddle The ratio of smaller peak to large

peak expressed as a percent

Valley Classify as "U" or' "V" shapped

Draw Ratio length to width

Classify as "U" or "V" shapped

Floor and Ratio length to width

Plateau

Vegetation The longest dimension and the

percent of width to longest dimension

Ponds The longest dimension and the

percent of width to longest dimension

ELEVATION PARAMETERS

Table 3 defines the elevation measure for specific

features.

S LOPE PARAMETERS

Slopes are classified in three categories as follows:

1. Uniform

2. Convex

3. Concave

Slope is also represented by its steepness angle.

ORIENTATION PARAMETER

Orientation of a feature is the angle between its long

axis (if one exists) and North. Orientation of an edge of a

feature is the angle between that edge and North.

87
TYPE OF WOODS

Trees are classified into two types: deciduous and

coniferous.

LIGHT BEACON PARAMETER

A man-made feature known to provide a source of light

at night (e.g., beacon tower, town). !I

FEATURE LOCATION

Prior to terrain coding, each route was divided into

intervals 500 meters (1 ,000 meters for day data) in length. The

center of each interval is used as a reference point for the

interval.

The location of a feature relative to an interval reference

point is coded using a polar overlay containing a grid as shown

in Figure 1. The relative location of a feature is recorded according

to the grid cell (or cells) which contains the feature. The polar

overlay divides the area around the reference point into eight

bearing and four range segments.

ROUTE CODING

Information recorded for each interval reference

point includes:

Route number

Reference point sequence number

Heading to next point

Distance to next point

Route elevation

Primary vegetation in area

Number of terrain and elevation features

88
Figure Al

POLAR OVERLAY "STAR CODE"

Eight sectors of

45° each

North Sector

Bearng 81 Numbers

~Sectors

7 Origin

IV ange Rings

Range Ring Radius Dimensions

I 250 meters

II 500 meters

Ill 1000 meters

IV greater than 1000 meters

89
For each feature identified within the overlay area, its name,

location, and other parameters are coded. Table 5 defines the

code number for each type of feature.

The method of coding the location of a feature with

respect to an interval reference point is accomplished as follows:

1. Identify the next interval reference point

along the route.

2. Place origin of overlay on reference point

maintaining North orientation.

3. Determine and record the sections containing

each feature.

A binary code is used to identify the combination of

sectors containing a feature. As an example, assume that a

feature is contained in sectors one, three, and four and

range rings one and two. The sector code is equal to

9 = 20(for sector one)+ 2 1 (for sector two)

+ 23 (for sector four).

The range code for a feature in range sectors one and two

is computed in a similar way:

2o (for sector one) + 21 (for sector two)

90

LL I,,
Table A5

FEATURE NAME, CODES

Feature Name Code

Elevation

, Ridge 10

Hill 11

Finger 12

Saddle 13

Valley 14

Draw 15

Floor 16

Plateau 17

Vegetation

Dense woods 20

Woods 21

Scattered trees 22

Orchard 23

Marsh 24

Scrub 25

Cultivated 26

Clearing 27

Hydrology

River 30

Stream 31

Pond 32

Man-Made

Highway 40

Road 41

Railroad 42

Building 43

Developed area 44

Tower 45

Water tank 46

Power transmission lines 47

Dams 48

Bridges 49

91
APPENDIX B

COMPUTER PROGRAM DOCUMENTATION FOR THE ANALYSIS

OF NIGHT TACTICAL NAVIGATION DATA

INTRODUCTION

The computer programs documented herein can search,

under user control, data files containing night tactical helicopter

navigation data. The purpose of the programs is to permit

search of the navigation data file to locate terrain of a specified

type and to determine the number of successful and unsuccessful

attempts to navigate that type of terrain. The terrain type is

specified by the user. Output of the programs consist of printout

revealing the number of subject navigator attempts, successes

and probability of success (PS) for each subject and each route.

Associated with the computer programs are: a data

file which contains terrain data (on magnetic tape), input cards

which are punched by the user to specify the type of terrain

features of interest, input cards which identify location of subject

navigation errors, and job control language (JCL) cards which

provide instructions to the computer for running the programs.

Figure 1 is a flow diagram for the system. In using

the system, the user selects terrain features and feature parameters

of interest, and prepares "input parameter cards" representing

the desired terrain features. These cards are the only input

required by the user.

JCL cards, input parameter cards, subject error

location cards, and program cards are input to the computer.

Proper ordering of the cards is described in a subsequent section.

Once the cards are input all data search calculations are automatic

including generation and reading of the intermediate file "matched

interval file." The computer produces the desired output printout.

Each of the card systems, files, output and programs are

documented in the following sections.

92
Fire BI

SYSTEM FLOW DIAGRAM

Selection

of Run

aramete

Input

Parameter

Program Matched

FMATH aw interval

Listing

Mathe
INPUT CARDS

TERRAIN FEATURES AND FEATURE PARAMETERS

The first terrain feature card specifies valley and

clearing classifications (see final report for discussion of valley

classes A, B, C). This card must be included in each run.

Additional terrain cards are used to specify the terrain

type of interest. Two cards (a set) are used to specify each

terrain feature to be included or excluded in the search. Up to

50 terrain feature cards (i.e., 50 sets of cards) can be specified;

but, at least one set must be included.

Formats for these cards are as follows:

Valley and Clearing Class Card Format

Search Parameter Input Values Format

1. Valley class* 0,1,2,3 15

2. Clearing class* 0,1,2 15

*Class 0 includes all intervals

1 includes only intervals in Class A

2 includes only intervals in Class B

3 includes only intervals in Class C

**Class 0 includes all intervals

1 includes only intervals with no clearing

2 includes only intervals with clearings

Card 1 Format is 215

94
Terrain Feature and Feature Parameter Card Formats

CARD #1

Card

Search Parameter Input Values Item# Column

1 . Feature type Number code I 1 - 5

for feature

(Table 1)

2. Match code 1, -1 ** 2 6- 10

3. Minimum number of Integer >_ 0 3 11 - 15

features required for

pattern match

4. Maximum number of Integer ? 0 4 16 - 20

features required for

pattern match

5. Blank 5

Blank 6

Blank 7

6. Range

A. Zone I 1 8 36-40

B. Zone 2 1,-i 9 41 -45

C. Zrne 3 1, -1 10 46-50

7. Orientation

A. N-S 1, 1** 11 51 -55

B. NNE-SSW 1,-i 12 56-60

C. ENE -WSW 1,-1 13 61 -65

D. E-VV 1,-i 14 66-70

E. ESE-WNW 1,-1 15 71 - 75

F. SSE-NNW -1 16 76-80

95
Card

Search Parameter Input Values Item # Column

CARD #2

a. Length of Features (meters)

A. Minimum length of Integer > 0 17 1 - 5

feature for pattern

match

B. Maximum length of Integer > 0 18 6- 10

feature for pattern

match

9. Width of Features (meters)

A. Minimum width of Integer > 0 19 11 - 15

feature for pattern

match

B. Maximum width of Integer > 0 20 16 - 20

feature for pattern

match

10. Elevation of Features (feet

above sea level)

A. Minimum elevation Integer ? 0 21 21 - 25

of feature for pattern

match

B. Maximum elevation Integer > 0 22 26 - 30

of feature for pattern

match

11. Vegetation (for elevation 0 for all types 23 31 - 35

features only) of vegetation or

vegetation code

12. Finger Elevation Change

A. Minimum elevation Integer > 0 24 36 - 40

change for pattern

match

B. Maximum elevation Integer > 0 25 41 - 45

change for pattern

match

13. Finger Elevation Change

Type

A. Uniform 1, -1 ** 26 46-50

B. Convex 1, -1 27 51 -55

C. Concave 1, -1 28 56-60

D. Compond 1, -1 29 61 -65

*Format for all items is I5, Card 2 format is 1616, Card 3 format is 1315

**I = include feature

-1 = exclude feature

96
Table Bi

LISTING OF TERRAIN FEATURES

Class ID Type of Terrain Number of Features

Number Feature in Class

1 Ridge None

2 Ridge 1 or more

3 Hill None

4" Hill 1, 2, or 3

5 Hill 4 or more

6 F inger None

Y Finger 1, 2, or 3

8 Finger 4 or more

9 Valley None

10 Valley 1 Only

11 Valley 2 or more

12 Draw None

13 Draw 1, 2, or 3

14 Draw 4 or more

15 Plateau None

16 Plateau I or more

17 Rivers None

18 Rivers 1 Only

19 Rivers 2 Only*

20 Stream None

21 Stream 1 or 2

*More than two never occurred in an interval

97
7ableEl (Concluded)

LISTING OF TERRAIN FEATURES

Class ID Type of Terrain Number of Features

Number Feature in Class

22 Stream 3 or more

23 I. Stream None

24 I. Stream 1 or 2

25 I. Stream 3 or more

26 Pond None

27 Pond 1 Only

28 Pond 2 or more

29 Highway None

30 Highway 1 or more

31 Road None

32 Road 1 or 2

33 Road 3 or more

34 Railroad None

35 Railroad 1 or more

36 Building None

37 Building 1 or more

38 Dev. Area None

39 Dev. Areea 1 or more

40 Power Line None

41 Power Line 1 or more

42 Dam None

43 Dam 1 or more

44 Bridge None

45 Bridge 1 or more

"More than one never occurred in an interval

98
SUBJECT ERROR LOCATION (F 1 )

The subject error location file has been prepared and need

only be input with each computer run. Two types of cards are

used: one (Type 1) specifying the number of errors and missed

route intervals along a route for each subject and the other card

(Type 2) identifying the location of each navigation error. Formats

for these cards are as follows:

CARD TYPE #1 (One card per subject per route)

Card

Item Format Column

1. Error Summary

A. Route number 15 1 - 5

B. Subject number 15 6 - 10

C. Attempt code* 15 11 - 15

D. Number of errors on route 15 16 - 20

E. Number of misses on route 15 21 - 25

CARD TYPE #2 (One card per error or missed interval)

2. Error Summary

A. Flag** 15 1 - 5

B. Location(elapsed meters on route) 15 6 - 10

*-.1 if subject did not attempt route, otherwise entry should be 1

**Flag 0 if miss, 1 if error

FILE FORMATS

Formats for three files: terrain feature data file (F 2 ),

matched interval file F3 , and PS output file F 4 are qiven in

Tables 2, 3, and 4 respectively.

JOB CONTROL LANGUAGE

The JCL required to run the program is listed in Table 5.

PROGRAM OUTPUT FORMAT

A sample program output reflecting example data is given

in Table 6.

PROGRAM LISTING

A listing of the computer programs is given in Table 7.

99
Table B2

TERRAIN FEATURE DATA FILE

Item Format

(1) Formats for feature interval definition

1. Route number 13

2. Sequence number 13

3. Distance from sequence number point 15

4. Lower boundary of interval* 17

5. Upper boundary of interval* 17

6. Number of possible elevation features 13

7. Number of possible man-made features 13

8. Number of possible water features 13

(2) Elevation Features

I. Route number 13

2. Sequence number 13

3. Feature type 13

4. Center location, X coordinate** 14

5. Center locatiorn Y coordinate 14

6. Length I5

7. Width 15

8. Slope type 14

9. Slope amount 14

10. Associated vegetation 14

11. Orientation 14

12. Elevation 14

13. Bearing at sequence number 14

14. Range at sequence number 14

15. Bearing at next sequence number 14

16. Range at next sequence number [4

17. Bearing from interval midpoint 14

18. Range from interval midpoint 14

*Elapsed meters on route path

*Center location is found at the first point from which a feature

is coded and except for hills, fingers, bridges, dams, ponds,

they do not represent the actual location of the feature.

100
........... _, . .. • .... . _ ,_ ..

Table B2(Concluded)

TERRAIN FEATURE DATA FILE

Item Format

(3) Man-made Features

1. Route number 13

2. Sequence number 13

3. Feature type 13

4. Center location, X coordinate 14

* 5. Center location, Y coordinate 14

6. Orientation 14

7. Bearing at sequence number 14

8. Range at sequence number 14

9. Elevation 14

10. Bearing at next sequence number 14

11. Range at next sequence number 14

12. Bearing from interval midpoint 14

13. Range from interval midpoint 14

(4) Water Features

1. Route number 13

2. Sequence number 13

3. Feature type 13

4. Center location, X coordinate 14

5. Center locatior Y coordinate 14

6. Orientation 14

7. Bearing at sequence number 14

8. Range at sequence number 14

9. Length 14

10. Width 14

11. Elevation 14

12. Bearing at next sequence number 14

13. Range at next sequence number 14

14. Bearing at interval midpoint 14

15. Range at interval midpoint 14

101
Table B3

FORMAT OF "MATCHED INTERVAL FILE"

Item Format

1. Route number 16

2. Sequence number 16

3. Distance from sequence point 16

4. Lower boundary of interval 16

5. Upper boundary of interval 16

Table B4

FORMAT FOR P OUTPUT FILE

Item Format

1. PS Route 1 F7.3

2. PS Route 2 F7.3

3. PS Route 3 F7.3

4. PS Route 4 F7.3

5. PS Route 5 F7.3

102
Table B5

SYSTEM JCL

//ST1 EXEC FORTCLG

* //FORT.SYSIN DD

PROGRAM FMATCH

/1* PRINTER

//GO. FTO6FOO1 DD SYSOUT = A

/1* INTERVAL FEATURE FILE

I/GO. FTQ2FOO1 DD DSN=RINF.,DISP=-(OLD, KEEP),

/UNIT=TAPE9,VOL=SER=NTNOE, LAc3E L=1,

IDCB=-(RECFM=FB, LRECL=-120,BLKS12E=18000)

/1* MATCHED INTERVAL FILE

I/GO. FTO3FOO1 DD DSN=RMAT, DISP--(NEW, PASS),

IUNIT=SYSDA,SPACE=(TRK ,200),

/1DC8=-(RECFM=FB, LRECL=30, BLKSIZE=4500)

/1* INPUT PARAMETER FILE

//GO.FTIFO1 DD *

/1* DELETE FIRST PROGRAM

//ST2 EXEC PGM=IEFBR14

//DD1 DD DSN=&LOADSET,UNIT=SYSDA,

// DISP-=(OLD, DELETE)

//ST3 EXEC FORTGLG

//FORT.SYSIN DD *

PROGRAM CERROR

/1* PRINTER

//GO. FTO6FOO1 DD SYSOUT=A

/1* MATCHED INTERVAL FILE

I/GO. FTO1 FO0l DD DSN=RMAT, DISP=-(OLD, DELETE),

IUNIT=SYSDA

/1* SUBJECT ERROR LOCATION FILE

//GO.FT2FO1 DD *

/1* SUBJECT PS FILE

I/GO. FTO3FOO1 DD DSN-SUBPS ,UN IT=SYSDA,

IIDIS P-(N EVW,PASS), SPAC E=(TRK, 100),

IIDCB--(RECFM=F, LRECL--35,BLKS12E=35)

IBM (360/50)

103
Table 6

SAMPLE PROGRAM OUTPUT

COUNT OF ATTEMP.TS (A) AND SLCCESSEZ CS) FOR CEFINED rATTERN

KUMDER OF INTERVALS ROUTEPI 29 T!0UT!-#2 38 PORUTC03 31

FCUTE04 34

ALL 132

SUB ROUTEPI ROUTE#2 ROUTE#3 ROUTE#4 ALL S A S A S A S A S A

AriD- IB

5 19 22 30 35 30 31 31 32 110 120

6 0 0 15 19 30 31 34 34 79 84

7 27 29 35 38 28 30 32 34 122 131

8 27 29 34 37 30 31 33 34 124 131

17 25 26 34 3C 29 30 33 34 121 126

15 28 29 36 38 19 21 29 33 112 119

19 15 19 31 34 28 29 3C 32 104 114

20 18 22 26 28 £6 19 28 31 88 300

AND-3A

9 20 22 37 38 24 28 30 32 £11 12C

10 14 18 37 38 29 31 26 30 106 317

11 Is is 0 C 27 29 34 34 76 81

12 12 15 31 34 16 20 C 0 59 69

21 25 29 30 32 30 31 32 33 120 125

22 24 27 35 37 15 £8 31 33 105 115

23 26 29 34 38 30 31 31 33 121 131

KP-IC

13 24 2f 31 33 25 26 33 34 113 119

14 C 0 33 35 30 31 31 33 94 99

24 27 29 38 33 26 28 c 0 91 95

25 29 29 30 33 31 31 C C 90 93

21 29 29 38 38 31 31 33 34 331 132

27 29 29 38 38 31 31 33 34 131 132

TOTAL DY ROUTE

436 476 653 697 555 588 564 592 2208 2353

AND-I9

159 176 241 265 210 222 250 2C2 86C 925

AiD-3A

139 158 2C4 217 171 188 184 195 698 758

NP-IC

138 142 2C8 215 174 176 130 135 66C £70

104
Table B6 (Continued)

SAMPLE PROGRAM OUTPUT

PROBABLZTZES OF SUCCESS

SUBJECT ROUTE0I ROUTE#2 ROUTE#3 ROUT£04 ALL

AMD- IB

5 0.86364 0.85714 0.96774 0.9f875 C.91667

6 0.OCCC0O 0.78947 0.96774 !.0CcCC 0.94048

7 0.93103 0.92105 0.93333 C.94118 0.93130

8 0.93103 0.91892 0.96774 0.97059 0.94656

17 0.9£154 0.94444 0.96CE7 0.97059 0.96C32

18 0.96552 0.94737 0.9C476 0.93548 0.94118

19 0.78947 0.91176 0.96552 0.93750 0.91228

20 0.81813 0.92e57 0.84211 0.9C323 0.88C00

A4D-3A

9 0.90909 0.97368 0.85714 0.93750 0.9250C

10 0.77778 0.97368 0.93548 0.8(.7 G.90598

11 0.83333 0.000CC 0.93103 1.00000 0.93827

12 0.8000 0.91176 0.80000 0.OCC0o 0.855C7

21 0.96552 0.93750 0.96774 C.96970 0.96ccc

22 0.88889 0.94595 0.83333 0.93939 0.91304

23 0.89655 0.89474 0.96774 0.93939 0.9236C

NP-IC

13 0.92308 0.93939 0.96154 0.97059 0.94958

14 0.000c0 0.94286 0.96774 C.93939 C.94949

24 0.93103 I.0CoC0 0.92857 0.000CC 0.95789

25 1.00000 0.9c909 1.00C00 0.0C000 0.96774

26 I.OOCCC 1.00000 1.00000 0.97059 0.99242

27 1.00000 1.00C00 1.00000 0.97059 0.99242

BY POUTE

0.91597 0.93687 0.94388 0.95270 0.93838

AND-IB

0.90341 0.90943 0.94595 0.95420 0.92973

AI'D-3A 0.87975 0.94009 0.90957 0.94359 0.92C84

ur- IC

C.97183 0.96744 0.97753 0.96296 C.97015

AVErAGE OF SVLjECT rECLILILITIES

AI.3- ID

0.8943S 0.9C234 0.92945 0.95341 0.923(c

AI:D- 3A

0.3C731 C.9295S 0.2993 C.94211 C.917L9 ::T'- IC

I.LL

0.90451 0.93L37 C.93(47 L.95173 C.9361C

105
Table B6 (Continued)

SAMPLE PROGRAM OUTPUT

C *PPOG&AM C~kRCIR 4/24/lW

C 00 AR! C-?

C 4* TO LETERMIt4. THE NUMBER OF

C 44 ILNP75 AkiD SUCCESS FOR EACH

C *0SU(jLCT IFdk THE. INTERVALS OiF

C 0* F IN1EkLST

-I SII LPP(21 9~P45).PE( 4.931 IT T') P)b

D;lA LC/7-,0*0/,IC/30OD*/.1k1OT/50/Pi/5*O.0/.

-ISt/5.et7,(i.7l~,92Q9,10~l,1,2.2l,2Z22,

- -I',*L '9Ib'9'A'@*C 19 0/*SPIV#1C50.O/9

~- P/20O.U/.tPM/Z00/

CO

C kOtE.( LNTt.FVAL SET

DL 100 1-19150

IFtLC(I,1).EO.99) GO010 fto

IT 1t'II1 41

IkE =LC( 1.1)

1FAVT(JkIkJTOTI IRD)*1

]cc C(ANTIN.UE

C 0*

C ** SET ALL COjUNS 70 THE

C 4* VAXIMUM S.IP1OR. OF ATTEMIPTS

DD' 210 ImI94

U3 2c0 J=1921

20U CL 1 11JUJ210 CLI.TINUE

C 40 F~k EACH OF FOUR~ ROUTES

00 340 1=1.4

C ** 105% EACH CI- 21 POSSIBLk FLIGHTS

RL?.LZ,Z00D) ISbI14,IRT,I~kIERvIMS

15LII( 1II)~IN)

IH!1.C,.b)CC TO 300

C *0 FLI~hI NUT ILOWN

1((2s1RTISb)w0

Go 10 . 30

300 COt-7INUL

IRI.lLk*IMS

C 0* CHECK ID SEE~ IF- N4 HISSES OR ERRORS

IF(I1%C..LTj1 160 TO 330

C *0FUR EACH INTERVAL OiF INTEREST

00 320 K=19IRU

106
Table B6 (Continued)

SAMPLE PROGRAMA OUTPUT

REID01292100) 1TPtINPLOC

00 310 MzI,ITINT

I~fhl..LCP .J)GO TO 310

IFE (IMFLUC.L1 .LC(M#4) ).Ok.

(JMPL0C.GE.LCIM95))) (G0 TO 310

IF IIIP.LE.O)JC(IvIRTv1S'=

- L(1IIRlJ~h)-1

JF(I7P.LE.G)1Lf2,IRIISb)=

-IC(2.1ITI'B)-1

C 00 FOR Ef~kb(S

330 COP'71NUL

340 CU.1INUE

C

DO 4.10 1-1,4

D0 400 Jzl1.d

IC (2 I,JJ: IC .5 ,J IC2 lJ)

JC(1,59,0)=IC(1.b,930).IC(1,IJ3

400 CUNITNUE > 410f U1,71 NUL

C * c * PRINT. COUNTS

WP 17 U(6 ,9000) ( I RTO 7(UL=IP5 4

1.1 SU0 Kal,21

IF(K.Ecto13 6kll1E(6,950I I MAP( I,13.9MAI(192J,9PAI-( 10)

U(K.g6.9) 6RlTE(6,9501) MAPf29I)@MAP(2v2J.MA(2ipA,

-IC(1, ;tK) ,IC(293,K),IC(1,4,K),1C(2S.,K).1C(1,5,K),1C(2.5.K)

W 17L (6,920('

- jl ,93) ,IC(.3,30, IC(,,33.IU~.'.,3) l~e23

C 0-IC(I,!.,30.ICI2,3,30)9C143)lt&409

C ~*FINE MAP TrjlaLS ANDo PRIINT

DJ 510 KK&1,3

JI FP.U2 Sz

IPEKK.I&4.2) J$Pz15

IFIKK.L..3 ) JST*16

107
. ---- - ~ - - - - 4

Table B6 (Continued)

SAMPLE PROGRAM OUTPUT

D3 510 J*JSIYJSP

510 CUiNIINDI:

DCo 520 KKsI .3

WRIIL(W. 301) MAF(KKP1),MAP(KK,2)oMAP(KKi,3),

l CIC 1,KK,1) ,IMCIC 2,KK .1). IMC(1,tK .23.

- JICT(2,KJ(.2) .INCT(lKKv.3),IMCT(2,(Ka)91NC1( 1.KK,'.).

-IICI(2.KK,1.),IMCI(1.KK95),ImC7I2,KKo,I

520 CONTINUE

C 00

C 0*COPIFUT AND PRINT PROBAhLIIIES

Mki 6. 91. DO)

Do t30 Az=lid

IF(K.LC..1) kI4111A699501) t4AP(1,1).NArII.2),P1AFII,3)

00 £10 MaW

C2=FLUT (IL (2 ,MK))

IM C091t.,K).LLMO P14(14 1 L.0

IP(IC(e;IIS.3.GT.0) PN()CL/C.i

(.0CU!t.I INUk

03 (40 H=1*5

C1=FLUA1(IC(1 ,M*30))

C2zfLGA1 (IC.(2vMq30j)

(40 (2,r,36c LEO ) P#4(LI.L= .

t50 CONTINUE

WRITL(69970I) NPH(KK.L195)(K,)~P~(,

0 C60 K1INUL

C) 7L1. K(I,4(qK,)

JIuI1C~~KsIL.)Pfz

C 108
Table B6 (Concluded)

SAMPLE PROGRAM OUTPUT

IFIKK.Li..2) J!5Ja

I F MV. E 4..Z JSP&15

IF(kK.EQ.3) JSmib

IF(KI(.4S) JSPx2I

VO 720 mae

03 710 j-j5,ojsp

IFIJCIZIitJ).LC.0) GO 10 710

SNF(KK,P)&Sf4P(IKKq.1)41*O

7kU. C'KT.INUE

IF (Si4P(VKtMI*)G1.C.O) APM(I(K.Mi)sAPH(ICKM)/SNP(KM )

730 C(-?.IINUE

AS(lL~b#99D(I) MAP(KK#1)qMAPfVI(2)ps1AP(K~q3Is

( 4P(VKL A2

L. *4 W4J1UI fL$LT FILE 01F SULJLLI U(S)

WkIlLtj,3000) ($PM(KHMR *145J*

1(A) CLNIINUE

SILPA

1,Ut F:)i.MA I V I G

(LO FLIMATIGI$9CWJNI OF A17EMPI1 A) AINLS,

0 uCLSSS (S) FOR C&FINED PAT1I:kh/I I

- IUML,LI' CF It.TkIVALS'' go

- 'FUIL ?'I!'

- IOUIL 3'i!/, It

-'OUIL '4,915/ It

- *bUbt93X,'WU7E 1'.3X#$LU7E 2*v3X#POUTL 3',

-3X**kUU7E '%97XALL/S to

-3X9.50 !. As))

7l(0 FJkMAT(I OvxI391 F8.5)

* 4U1 FURM~TV Ot,3A2/'L b,3X.103L9

4 f. e I MAT46I's O'AVER&L IL SUF SCLEPROBJItII'

CD0 FEAIT( I32 99 ~'7X,!1?XFB.5)

47(0 FORP9ATIP ?.3) (X98*I

'. C1 lFkFlAll '.3A2/' '2 ,01

109 $4'
DISTRIBUTIONOUJPA I8.?1iJ

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112